Management Sciences




Department News

Stock Prognosticators? (6/11/14)

A first of its kind study by researchers at the University of Iowa suggests Yahoo’s finance message boards have a small degree of ability to predict stock price movements.

The study, “Stock Chatter: Using stock sentiment to predict price direction,” also found that more than two-thirds of the message board comments had nothing to do with finance.

The researchers analyzed 70,000 posts by more than 7,000 commenters on Yahoo’s finance message boards from April to June 2011. They determined what sentiment, if any, they expressed about 11 Fortune 500 stocks, either bullish, bearish, or neutral. The researchers then looked at the movement of those stocks’ prices the next day. Depending on the model the researchers used to classify the statements, they found that the sentiment expressed on the message boards accurately reflected the price movement anywhere between 52 and 64 percent of the time.

Michael Rechenthin, who conducted the study as a doctoral student in the Tippie College of Business, says that while the lower accuracy figures can be attributed to randomness, the 64 percent figure is statistically significant and shows a small degree of predictive ability.

The study found the predictive ability lasted only one day, though, and disappeared on follow-up days.

The study also found that only a small number of users produced the largest number of comments, and those comments seem to be responsible for whatever predictive quality the message boards had. Rechenthin says only 3 percent of the commenters wrote 50 percent of the posts during the study period, and 11 percent wrote 75 percent of the posts.

Those extreme users also tended to be the most opinionated, he says, and when they were removed from the study, what predictive ability the boards had all but disappeared. He says the study did not determine whether this was because the most forceful opinions affected stock price, or if those with stronger opinions were more seasoned market observers and able to see trends.

The study also found that, as one might expect of public message boards, many of the posts were completely irrelevant to the topic. They determined that 68 percent of posts were things like advertisements, spam, flame wars, or comments by trolls.

Politics tended to be the most common off-topic topics, he says, with President Obama and former President Bush frequent topics of criticism. The study period also included former Rep. Anthony Weiner’s resignation from Congress after texting inappropriate photos, and many comments were related to him.

Rechenthin cautions that as this is the first study of its type and follow-up studies will be needed using larger data sets. The research was published recently in the journal Algorithmic Finance and was coauthored with W. Nick Street, professor of management sciences in the Tippie College of Business, and Padmini Srinivasan, professor of computer science at the University of Iowa.

Drafty Research (5/6/14)

Jeff OhlmannWhen general managers gather in New York for the NFL draft this week, they’ll be awash in statistics, scouting reports, interview data, and video clips as they look for a way to digest it all and make the best draft selections for their respective teams.

How should the selection be made? They can use a rule of thumb, like filling the biggest hole in their roster, or picking the best player available. But how is best measured? University of Iowa professor Jeff Ohlmann says several teams are arming their staffs with information based on analytics in efforts to gain any edge that they can.

One of Ohlmann’s research focuses is how a sports team can optimize its draft selections, and this work has led to the development of a smartphone app to help fantasy football and baseball team owners pick their teams. Ohlmann says that a sports draft is a great example of what is called a “sequential decision-making problem with uncertainty,” an area of research that attracts interest of both academics and practitioners. For example, logistics companies need to design routes to supply goods without knowing exactly how much product will be required by the retailer or precisely how long it may take to get there.

In football, the uncertainty lies in not knowing how well a player may perform in the NFL, or even if that player will be available when a team is “on the clock” because some other team may pick him first. Using probability distributions to model the uncertainty, Ohlmann and other researchers have developed models that use mathematical techniques to maximize the expected value of the players which a team drafts. At its heart, the model tries to help general managers overcome the fundamental handicap of not knowing what players will be available to draft in future rounds.

In Ohlmann’s approach, a team projects opposing teams’ selections to forecast which players will be available to the team in future rounds. He says that while there will surely be errors in guessing which players other teams will select, the errors often cancel each other out to create a sufficiently accurate forecast of the draft as a whole. The result, he says, is a model that produces a draft strategy that typically dominates alternative drafting rules-of-thumb.

“Rules-of-thumb have flaws in them and you can do better if you try to predict what will happen in the future,” he says. “A draft strategy based on analytics isn’t guaranteed to dominate, but more often than not, it will be the best strategy.”

He acknowledges data analysis is not a crystal ball and will not be 100 percent accurate in identifying which players are going to be successful. “But it may help avoid a bad decision in cases when the front office personnel are 'fooled by their eyes’ and let emotions affect their decisions.”

Ohlmann, who teaches in the department of Management Sciences in the Tippie College of Business, has used his sports research to develop and teach a first-year seminar, Sports Analytics, to introduce analytical tools to students using a topic that many students are naturally interested in.

“My goal is to show students how to formulate sports-related questions and then use data and math to try to answer them rather than just qualitatively debating them,” he says.

Is Big Data Dating the Key to Long-Lasting Romance? (BBC News, 3/24/14)

If you want to know if a prospective date is relationship material, just ask them three questions, says Christian Rudder, one of the founders of U.S. Internet dating site OKCupid.

  • "Do you like horror movies?"
  • "Have you ever traveled around another country alone?"
  • "Wouldn't it be fun to chuck it all and go live on a sailboat?"

Why? Because these are the questions first date couples agree on most often, he says.

Mr. Rudder discovered this by analysing large amounts of data on OKCupid members who ended up in relationships.

Dating agencies like OKCupid, Match.com—which acquired OKCupid in 2011 for $50m (£30m)—eHarmony and many others, amass this data by making users answer questions about themselves when they sign up.

Some agencies ask as many as 400 questions, and the answers are fed in to large data repositories. Match.com estimates that it has more than 70 terabytes (70,000 gigabytes) of data about its customers.

Applying big data analytics to these treasure troves of information is helping the agencies provide better matches for their customers. And more satisfied customers mean bigger profits.

U.S. Internet dating revenues top $2bn (£1.2bn) annually, according to research company IBISWorld. Just under one in 10 of all American adults have tried it.

The market for dating using mobile apps is particularly strong and is predicted to grow from about $1bn in 2011 to $2.3bn by 2016, according to Juniper Research.

Porky pies

There is, however, a problem: people lie.

To present themselves in what they believe to be a better light, the information customers provide about themselves is not always completely accurate: men are most commonly economical with the truth about age, height, and income, while with women it's age, weight, and build.

Mr. Rudder adds that many users also supply other inaccurate information about themselves unintentionally.

"My intuition is that most of what users enter is true, but people do misunderstand themselves," he says.

For example, a user may honestly believe that they listen mostly to classical music, but analysis of their iTunes listening history or their Spotify playlists might provide a far more accurate picture of their listening habits.

Inaccurate data is a problem because it can lead to unsuitable matches, so some dating agencies are exploring ways to supplement user-provided data with that gathered from other sources.

With users' permission, dating services could access vast amounts of data from sources including their browser and search histories, film-viewing habits from services such as Netflix and Lovefilm, and purchase histories from online shops like Amazon.

But the problem with this approach is that there is a limit to how much data is really useful, Mr. Rudder believes.

"We've found that the answers to some questions provide useful information, but if you just collect more data you don't get high returns on it," he says.

Social engineering

This hasn't stopped Hinge, a Washington, D.C.-based dating company, gathering information about its customers from their Facebook pages.

The data is likely to be accurate because other Facebook users police it, Justin McLeod, the company's founder, believes.

"You can't lie about where you were educated because one of your friends is likely to say, 'You never went to that school'," he points out.

It also infers information about people by looking at their friends, Mr. McLeod says.

"There is definitely useful information contained in the fact that you are a friend of someone."

Hinge suggests matches with people known to their Facebook friends.

"If you show a preference for people who work in finance, or you tend to like Bob's friends but not Ann's, we use that when we curate possible matches," he explains.

The pool of potential matches can be considerable, because Hinge users have an average of 700 Facebook friends, Mr McLeod adds.

'Collaborative filtering'

But it turns out that algorithms can produce good matches without asking users for any data about themselves at all.

For example, Dr. Kang Zhao, an assistant professor at the University of Iowa and an expert in business analytics and social network analysis, has created a match-making system based on a technique known as collaborative filtering.

Dr. Zhao's system looks at users' behaviour as they browse a dating site for prospective partners, and at the responses they receive from people they contact.

"If you are a boy we identify people who like the same girls as you—which indicates similar taste—and people who get the same response from these girls as you do—which indicates similar attractiveness," he explains.

Dr. Zhao's algorithm can then suggest potential partners in the same way websites like Amazon or Netflix recommend products or movies, based on the behaviour of other customers who have bought the same products, or enjoyed the same films.

Internet dating may be big business, but no one has yet devised the perfect matching system. It may well be that the secret of true love is simply not susceptible to big data or any other type of analysis.

"Two people may have exactly the same iTunes history," OKCupid's Christian Rudder concludes, "but if one doesn't like the other's clothes or the way they look then there simply won't be any future in that relationship."

Dating Algorithm Makes Matches (KGAN, 2/14/14)

One of the hardest parts of dating is the uncertainty: will the person you like, like you back?

Finally, University of Iowa researcher Kang Zhao figured out an algorithm that matches up users with dates based on the likelihood that your crush will like you back.

"This algorithm can potentially find you a good date," Zhao said. "But whether it's a perfect match, whether you find your soul mate, you'll probably have to take it offline."

Zhao already has that figured out—he's been married to his wife, Tracy, for seven years. But, to do his research, he had to sign up for dating websites, too, something he had to clear at home, first.

"I just have to tell her, 'OK, I'm going to study this. I still love you. I still love our family,'" he said.

Zhao's algorithm is public domain, and he has already been contacted by some smaller dating websites to use it as a part of their platforms.

Machine Learning + Love (WNYC, 2/12/14)

Log onto an online dating site and you are asking a machine for romantic assistance. That's cool, but you might as well understand how it works, right?

There's an algorithm picking and choosing which profile to put in front of which users, and sometimes it works—roughly a third of marriages these days begin online—and other times it doesn't. On this week's New Tech City, host Manoush Zomorodi tracks down some smart people who are writing and improving the matching systems of dating sites.

Kenneth Cukier, data editor at The Economist, explains "you'd be a fool to try to do online dating without machine intelligence, without machine learning." So we get him to explain what that means. 

Kang Zhao, assistant professor of management sciences at the University of Iowa, is a very smart guy who has a plan to make sure the matches in front of you are people you'd actually like, and who will actually respond to your messages. "There are ways to improve [profiles] because the information you have in your profile is sometimes just too much."

And then we put all this to someone responsible for a whole lot of online meetings, V.P. of matching for eHarmony, Steve Carter, who says a few unexpected things, including that dating sites only work if you shake up your rigid mindset and embrace the real life, offline magic of face-to-face dating. 

UI Developed Algorithm Gives Online Dating a New Spin (KCRG, 2/5/14)

It's like Netflix, but for dating. A new algorithm at the University of Iowa is giving online dating a new spin.

The currently unnamed design matches users with compatible partners uses data about the person's past dating habits and levels of attractiveness. 

The method could be compared to the popular on-demand Internet streaming site, Netflix. It suggests movies to users based on their previous film and television choices. 

Kang Zhao, assistant professor at the Tippie College of Business, says this method is different from any other site out there. 

"So what we did is to look at your previous interaction history," he said. "We believe your actions speak louder than what you have in your profile."

The algorithm is still being developed. Zhao says he has spoken with executives at dating websites about using this new method of online dating. 

Predicting Stock Prices (1/31/14)

Predicting Stock Prices

Nick StreetA new study from the University of Iowa shows evidence that stock price movements are, in fact, predictable during short windows.

The study by researchers in the Tippie College of Business suggests that price movements can be predicted with a better than 50-50 accuracy for anywhere up to one minute after the stock leaves the confines of its bid-ask spread. Probabilities continue to be significant until about five minutes after it leaves the spread. By 30 minutes, the predictability window has closed.

The researchers—Nick Street, professor of management sciences, and doctoral student Michael Rechenthin—say the work questions the generally held belief that stock prices cannot be predicted. While factors like news or financial reports can move stock prices, the thinking holds, nothing inherent in a price’s trend line can be used to predict where the price goes next.

“This study is the first step in showing that there is predictability, and that once a price escapes the confines of the bid-ask spread, it’s showing a trend,” says Rechenthin, a former Chicago Stock Exchange floor trader whose dissertation looks at building models for predicting future stock price direction. “In other words, it’s more than just a coin flip where the price goes.”

The study examined price movements of a single stock—the S&P 500 exchange traded stock fund (SPY)—during 2005. The stock holds all 500 Standard and Poor’s stocks and is considered representative of the overall U.S. market. It’s also one of the most heavily traded equities on the market, with an average of more than 90,000 transactions a day during the study period, so it provides a wealth of study data.

Michael RechenthinTheir analysis found no predictability of the stock’s price within the bid-ask spread—that is, the space between the price that buyers are willing to pay for a stock (the bid) and the price sellers are willing to sell it for (the ask)—as the market tries to set the value of an asset. The key to their study is what happens once traders did set a value and the price escaped that spread. Once it did escape, the study tracked the stock’s price at 1, 3, 5, 10, and 20 seconds, and 1, 5, and 30 minutes.

The study found the stock price typically broke the spread after five to ten seconds, and the predictability of its subsequent movements depended on the pattern of its most recent trades. For instance, if the stock’s two most recent trades were an uptick followed by a downtick, there was a 52 percent probability the trend reversed itself within five seconds. Within 20 seconds, it had a 43 percent probability of reversal.

Rechenthin says these trends are driven only by previous trade prices because other factors that drive price—news or financial statements—cannot be incorporated into a price in such a short window.

While a 52 percent probability may not seem like much of a better probability than 50 percent, Street points out that in the ocean of data that is stock trading, it is a notable increase, and something that can be exploited. The next step is to develop a working model that takes advantage of these probabilities for more efficient trading.

The study, “Using Conditional Probability to Identify Trends in Intra-day High-Frequency Equity Pricing,” was published recently in the journal Physica A.

UI Team's Dating Algorithm Could Mean Better Matches (Iowa City Press-Citizen, 1/5/14)

University of Iowa researchers have uncovered a curious pattern in human behavior when it comes to online dating services: People think they know what type of person they want to date, but their behavior online says otherwise.

So, how do you find the perfect match?

Kang Zhao, assistant professor of management sciences at UI's Tippie College of Business, and UI doctoral student Xi Wang have created an algorithm for dating sites that uses a person's contact history to recommend potential partners.

"Your actions speak louder than your profile," Zhao said. "Your profile might say you like tall women, but all the women you contact are short."

The algorithm takes that disconnect into account by combining two factors: a person's taste (what type of person they approach) and a person's attractiveness/unattractiveness (how many of those people reciprocate that contact).

"You have to like someone to approach them, and there must be some sort of attraction for them to respond," Zhao said. "A person's unattractiveness is reflected by those you approached but didn't get back to you. Maybe they were out of your league.

"We can recommend potential partners in the same league with you," he said.

So far, Zhao has been approached by three different online dating services interested in using his algorithm. He declined to name those services.

During their research, Zhao and Wang used data provided by a popular commercial online dating company whose identity is being kept confidential. They looked at 475,000 initial contacts involving 47,000 users in two U.S. cities over a 196-day span. The users included 28,000 men and 19,000 women. Men made 80 percent of the initial contacts.

Zhao said that according to his data, only 25 percent of those initial contacts were reciprocated. But when the algorithm is applied, those return rates jump to 44 percent.

"Your previous actions are a better reflection of your tastes," Zhao said.

Wang said he used online dating services in the past but was always disappointed with the results.

"I wasn't interested in the people they recommended," she said. "I think with our model I would get better recommendations."

The algorithm is similar to the one used by Netflix, which recommends movies based on a costumer's viewing history. Zhao said his algorithm also could be used for other services that match people with something they are searching for online, such as jobs and colleges.

Another phenomenon Zhao discovered was that his algorithm worked best for men with "athletic" body types connecting with women with "athletic" or "fit" body types, and for women who indicate they "want many kids." The model also works best for users who upload more photos of themselves.

"The more active you are" on the dating website, the better this algorithm is in selecting your partner, Zhao said. "The men who are more athletic are probably more active on the dating site because they are more confident, and the same is true for women who are more athletic or fit."

Zhao's study was coauthored by Mo Yu of Penn State University and Bo Gao of Beijing Jiaotong University. It will be published in an upcoming issue of IEEE Intelligent Systems.

Can a New Online Dating Algorithm Transform Your Love Life? (Refinery 29, 12/11/13)

Much like gluten-free beer or tank tops with built-in bras, online dating is one of those things that's better in theory than in actual practice. Sites and apps promise to use the massive amounts of data on users' tastes and preferences to find them the love of their life. But, as anyone who has ever been brave enough to hand over their love life to a series of remote servers knows all too well, things can get pretty unsavory, pretty quick. The Internet, apparently, is filled with ego-centric a-holes (who aren't even that cute!)—and, no matter how many success stories you hear from your friends and those eHarmony commercials, it's all too easy to lose hope in the bleak, depressing realm of online matchmaking.

But, maybe we shouldn't delete our Match.com accounts quite yet. Professor Kang Zhao, a researcher at the University of Iowa, has come up with a new algorithm that promises to improve the capacity of websites to provide users with desirable (and compatible) matches. Whereas most of the existing systems are based solely on what users say they find attractive, Zhao's "hybrid" algorithm combines data about both your tastes and your attractiveness to others. That is, information about who you're attracted to, who you are messaging (and who messages back), and who finds you attractive, is compared to that of other users, creating a scoring system that places each user on a multidimensional grid of compatibility. In other words, the new system could go a long way toward connecting users with potential matches that they not only find attractive, but who are likely to message them back. Take that, OKCupid.

A New Online Dating Algorithm Will Match You With Someone You Might Actually Have A Chance With (Business Insider, 12/11/13)

Online dating grows ever more popular in our digital world.

Dating sites are far more effective if they are capable of matching up people who are actually likely to talk to each other. But the goal of finding good matches is a difficult one.

Recently, a research team led by Professor Kang Zhao at the University of Iowa has developed a better algorithm for dating sites to link up singles.

Matching heterosexual couples on a dating site is in many ways similar to matching users to movies on Netflix, or matching buyers to products on Amazon. We have two sets—men and women, users and movies, buyers and products—and we want to find a way to appropriately match up members of the first set to members of the second set.

Collaborative Filtering

There is, of course, a glaring difference between dating and the other matchings—the "targets" being chosen are human beings, and they can choose whether or not to reply. If I want to watch House of Cards on Netflix, Kevin Spacey cannot say no to me. If I message an attractive woman on a dating website, it is up to her whether or not to write a reply message.

Sites like Netflix and Amazon use a process called collaborative filtering ro make movie or product recommendations. The algorithm first compares me to other users, seeing how much overlap there is between the movies I watched and rated highly, and the movies that the other users watched and rated highly. This gives me a similarity score with other users—someone who, like me, has recently watched a lot of Star Trek on Netflix will have a high similarity score to me, whereas someone who exclusively watches romantic comedies from the '90s will have a very low similarity score to me.

Next, to make recommendations to me, for each movie that I have not seen, the algorithm calculates a score based on how that movie was rated by people with high similarity scores to me. Netflix recommends movies that were highly rated by people who like similar movies to me.

Zhao's Innovation

In the online dating context, an algorithm can get a good idea of my taste in partners by doing a similar comparison of me to other male users. Another male user of the site will have a similar taste in women to me if we are messaging the same women.

However, while this gives the algorithm a good idea of who I like, it leaves out the important factor of who likes me—my attractiveness to the female users of the site, measured by who is sending me messages.

Zhao's crucial innovation is to combine information about both tastes and attractiveness. The algorithm keeps track of both who I am messaging, and who is messaging me. If a male user has similar taste (he is messaging the same women as I am) and attractiveness (he is messaged by the same women as I am) to me, we are scored as being very similar; if we are similar in one trait—if we have similar tastes but attract (or fail to attract) different groups of women, or vice versa—we have a moderate similarity ranking, and if we are different on both measures, we are counted as very dissimilar.

Similarly, when finding women to recommend to me, the algorithm factors in both sides of the messaging coin. Women who had a back-and-forth messaging relationship with men similar to me are ranked very highly, women who had a one-sided messaging relationship with men similar to me are ranked in the middle, and women who have had no contact on either side with similar men are left out.

Zhao and his peers tested their hybrid algorithm, incorporating both taste and attractiveness information, on an unnamed popular dating site, and found that it outperformed a number of other recommender models. The algorithm did a very solid job in recommending potential matches that, if messaged, would message users back.

While online dating, like all dating, is still a very uncertain path to finding love, innovations like Zhao's can help dating sites become ever better at matching people up with each other.

Need Love? There's a New Algorithm for That (Jezebel, 12/11/13)

Looking for love online? How about a new algorithm that finds the person who is also looking for, and will like, you? Well, it's a thing now, thanks to Professor Kang Zhao at the University of Iowa, writes Business Insider.

Zhao's crucial innovation is to combine information about both tastes and attractiveness. The algorithm keeps track of both who I am messaging, and who is messaging me. If a male user has similar taste (he is messaging the same women as I am) and attractiveness (he is messaged by the same women as I am) to me, we are scored as being very similar; if we are similar in one trait—if we have similar tastes but attract (or fail to attract) different groups of women, or vice versa—we have a moderate similarity ranking, and if we are different on both measures, we are counted as very dissimilar.

Similarly, when finding women to recommend to me, the algorithm factors in both sides of the messaging coin. Women who had a back-and-forth messaging relationship with men similar to me are ranked very highly, women who had a one-sided messaging relationship with men similar to me are ranked in the middle, and women who have had no contact on either side with similar men are left out.

Zhao and his peers tested their hybrid algorithm, incorporating both taste and attractiveness information, on an unnamed popular dating site, and found that it outperformed a number of other recommender models. The algorithm did a very solid job in recommending potential matches that, if messaged, would message users back.

Now if only Zhao would give up which site he tinkered with, this could be a very happy holiday season.

University of Iowa Professor Revamps Online Dating with New Algorithm (Philly.com, 12/11/13)

The online dating world is bereft with tales of dates gone wrong, poorly matched partners struggling to find the connection that the dating site they use saw between them. But no more, thanks to Professor Kang Zhao's work at the University of Iowa.

Matches in online dating all come down to an algorithm, like those used on popular search engines to aggregate and arrange content in order of popularity and relevance to your search. Only with dating, we're talking about more intangible factors and evaluating an individual's personality and appearance subjectively rather than simply trying to find the most relevant information. Zhao's algorithm moves those intangibles to a more objective data set, resulting in more accurate matches.

But perhaps it's best to let the experts explain:

"Zhao's crucial innovation is to combine information about both tastes and attractiveness. The algorithm keeps track of both who I am messaging, and who is messaging me. If a male user has similar taste (he is messaging the same women as I am) and attractiveness (he is messaged by the same women as I am) to me, we are scored as being very similar; if we are similar in one trait—if we have similar tastes but attract (or fail to attract) different groups of women, or vice versa—we have a moderate similarity ranking, and if we are different on both measures, we are counted as very dissimilar. 

Similarly, when finding women to recommend to me, the algorithm factors in both sides of the messaging coin. Women who had a back-and-forth messaging relationship with men similar to me are ranked very highly, women who had a one-sided messaging relationship with men similar to me are ranked in the middle, and women who have had no contact on either side with similar men are left out." 

The new "hybrid algorithm" was then tested on an as-yet-unnamed dating site. The findings were promising, with their new algorithm outperforming a number of existing popular models.  And all that just to get someone to message you back.

Now only if we knew what site they tinkered with.

Researchers: Dating Sites Have It All Wrong (Consumer Affairs, 12/9/13)

While it is true that some people successfully find good, lasting relationships on online dating sites, it is also true that many end up frustrated and disappointed.

Rochelle, a Match.com user from Irvine, Calif., says she has found a troubling pattern with the men she has met online: they aren't telling the truth, she says.

"I've noticed that a lot of men are lying about their age," Rochelle writes in a ConsumerAffairs post. "I set my age limit at 45 and about a quarter of the men contacting me are no way even close to 45. Try 55-65! Also, a lot of men use very old pics. Sorry, but any picture older than 2-3 years is irrelevant."

Disconnect

Researchers at the University of Iowa (UI) think Rochelle might unknowingly be onto something. Not that people are dishonest when they use an online dating site but there's a disconnect—what they say doesn't really match what they truly want.

Kang Zhao, assistant professor of management sciences in UI's Tippie College of Business, and UI doctoral student Xi Wang are part of a team that has developed an algorithm for dating sites that uses a person's contact history to recommend partners with whom they may be more romantically compatible.

Netflix model

It's similar to the model Netflix uses to recommend movies users might like by tracking their viewing history. For example, you might not pick a particular movie to watch but Nexflix, analyzing the movies you've watched in the past, says "hey, you might like this one." In a way, it's putting the computer in computer dating.

Dating sites are taking notice. Zhao says he's had preliminary discussions with two dating services who have expressed interest in learning more about the model. Since it doesn't rely on profile information, Zhao says it can also be used by other online services that match people, such as a job recruiting or college admissions.

The system was developed with the help of a popular commercial online dating company whose identity is being kept confidential. The research team looked at 475,000 initial contacts involving 47,000 users in two U.S. cities over a 196-day span. Of the users, 28,000 were men and 19,000 were women, and men made 80 percent of the initial contacts.

The data showed that just 25% of those initial contacts were actually reciprocated. To improve that results, Zhao's team developed a model combining two factors to recommend contacts: a client's tastes, determined by the types of people the client has contacted; and attractiveness/unattractiveness, determined by how many of those contacts are returned and how many are not.

Better predictor

Zhao believes those two factors, taste and attractiveness, do a better job of predicting successful connections than relying on information that clients enter into their profile, because what people put in their profile may not always be what they're really interested in. And from Rochelle's observation, they could also be intentionally misleading.

Zhao goes a step further, suggesting the average user of an online dating site might not really know themselves well enough to know their own tastes in the opposite sex. A man who says on his profile that he likes tall women may in fact be approaching mostly short women, even though the dating website will continue to recommend tall women.

"Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao says.

Another way of saying, actions speak louder than words. Zhao says that eventually, the algorithm will notice that while a client says he likes tall women, he keeps asking out short women, and will change its recommendations to start suggesting that he contact short women.

If it works for movies, it should work for dates, Zhao says.  

Why the Future of Online Dating Relies on Ignoring You (Forbes, 12/7/13)

According to a new study, Netflix and Amazon have much to teach online dating sites. Netflix doesn't wait around for you to tell it what you want; its algorithm is busily deciphering your behavior all the time to figure it out. Likewise, say researchers, dating sites need to start ignoring what people put in their online profiles and use stealthy algorithmic logic to figure out ideal matches—matches that online daters may have never pursued on their own.

Kang Zhao, assistant professor of management sciences in the University of Iowa Tippie College of Business, is leading a team that developed an algorithm for dating sites that uses a person's contact history to recommend partners with whom they may be more compatible, following the lead of the model Netflix uses to recommend movies users might like by tracking their viewing history.

The difference between this approach, and that of using a user's profile, can be night and day. A user's contact history may in fact run entirely counter to what she or he says they are looking for in a mate, and usually they aren't even aware of it.

Zhao's team used a substantial amount of data provided by a popular commercial online dating service: 475,000 initial contacts involving 47,000 users in two U.S. cities over 196 days. About 28,000 of the users were men and 19,000 were women, and men made 80 percent of the initial contacts. Only about 25 percent of those contacts were reciprocated.

Zhao's team sought to improve the reciprocation rate by developing a model that combines two factors to recommend contacts: a client's tastes, determined by the types of people the client has contacted; and attractiveness/unattractiveness, determined by how many of those contacts are returned and how many are not.

"Those combinations of taste and attractiveness," Zhao says, "do a better job of predicting successful connections than relying on information that clients enter into their profile, because what people put in their profile may not always be what they're really interested in. They could be intentionally misleading or may not know themselves well enough to know their own tastes in the opposite sex."

Zao gives the example of a man who says on his profile that he likes tall women, but who may in fact be approaching mostly short women, even though the dating website will continue to recommend tall women.

"Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao says. The research team's algorithm will eventually "learn" that while a man says he likes tall women, he keeps contacting short women, and will unilaterally change its dating recommendations to him without his notice, much in the same way that Netflix's algorithm learns that you're really a closet drama devotee even though you claim to love action and sci-fi.

"In our model, users with similar taste and (un)attractiveness will have higher similarity scores than those who only share common taste or attractiveness," Zhao says. "The model also considers the match of both taste and attractiveness when recommending dating partners. Those who match both a service user's taste and attractiveness are more likely to be recommended than those who may only ignite unilateral interests."

After the research team's algorithm is used, the example 25 percent reciprocation rate described above improves to about 44 percent—a better than 50% jump.

Zhao says that his team's algorithm seems to work best for people who post multiple photos of themselves and also for women who say they "want many kids," though the reasons for that correlation aren't quite clear.

If you're wondering how soon online dating services could start overruling your profile to find your best match, Zhao's team has already been approached by two major services interested in using the algorithm. And it's not only online dating that will eventually change. Zhao adds that college admissions offices and job recruiters will also benefit from the algorithm.

The age of Ignore is upon us, though safe money says we'll continue thinking we've "chosen" the outcomes anyway.

UI Researchers: Netflix-Style Tracking Can Increase Online Dating Success (Des Moines Register, 12/4/13)

Online daters looking for a virtual love connection can look no further than Netflix's recommendations feature.

University of Iowa researchers say the algorithm that gives viewers suggestions on the popular movie site could help them find a potential mate online.

Assistant professor Kang Zhao and student Xi Wang have created a model that would use someone's contact history to recommend more compatible partners, according to a news release. Netflix recommends movies to users based upon viewing history.

The conclusion came after a study used data from 47,000 users of an undisclosed popular dating website to seek a better solution.

"Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile," Zhao said.

The new approach takes into account what a user does, which Zhao says is not always the same as what a person puts on his or her personal profile.

For the complete study, which was coauthored by Mo Yu of Penn State University and Bo Gao of Beijing Jiaotong University, see below. To see the complete news release, visit the University of Iowa's news website.

Love Connection (12/4/13)

Love Connection

Most online dating users don’t choose a potential mate the same way they choose a movie to watch, but new research from the University of Iowa suggests they’d be more amorously successful if that’s how their dating service operated.

Kang Zhao, assistant professor of management sciences in the Tippie College of Business, and UI doctoral student Xi Wang are part of a team that recently developed an algorithm for dating sites that uses a person’s contact history to recommend more compatible partners. It’s similar to the model Netflix uses to recommend movies users might like by tracking their viewing history.

Zhao’s team used data provided by a popular commercial online dating company whose identity is being kept confidential. It looked at 475,000 initial contacts involving 47,000 users in two U.S. cities over a 196-day span. Of the users, 28,000 were men and 19,000 were women, and men made 80 percent of the initial contacts.

Zhao says the data suggests that only about 25 percent of those initial contacts were actually reciprocated. To improve that rate, Zhao’s team developed a model that combines two factors to recommend contacts: a client’s tastes, determined by the types of people the client has contacted; and attractiveness/unattractiveness, determined by how many of those contacts are returned and how many are not.

Those combinations of taste and attractiveness, Zhao says, do a better job of predicting successful connections than relying on information that clients enter into their profile, because what people put in their profile may not always be what they’re really interested in. They could be intentionally misleading, or may not know themselves well enough to know their own tastes in the opposite sex. So a man who says on his profile that he likes tall women may in fact be approaching mostly short women, even though the dating website will continue to recommend tall women.

“Your actions reflect your taste and attractiveness in a way that could be more accurate than what you include in your profile,” Zhao says. Eventually, Zhao’s algorithm will notice that while a client says he likes tall women, he keeps contacting short women, and will change its recommendations to him accordingly.

“In our model, users with similar taste and (un)attractiveness will have higher similarity scores than those who only share common taste or attractiveness,” Zhao says. “The model also considers the match of both taste and attractiveness when recommending dating partners. Those who match both a service user’s taste and attractiveness are more likely to be recommended than those who may only ignite unilateral interests.”

While the data Zhao’s team studied suggests the existing model leads to a return rate of about 25 percent, Zhao says a recommender model could improve such returns by 44 percent.

When the researchers looked at the users’ profile information, Zhao says they found that their model performs the best for males with “athletic” body types connecting with females with “athletic” or “fit” body types, and for females who indicate that they “want many kids.” The model also works best for users who upload more photos of themselves.

Zhao says he’s already been contacted by two dating services interested in learning more about the model. Since it doesn’t rely on profile information, Zhao says it can also be used by other online services that match people, such as job recruiting or college admissions.

Zhao’s study, “User Recommendation in Reciprocal and Bipartite Social  Networks—A Case Study of Online Dating,” was coauthored by Mo Yu of Penn State University and Bo Gao of Beijing Jiaotong University. It will be published in a forthcoming issue of the journal IEEE Intelligent Systems and is available online at arxiv.org/pdf/1311.2526v1.pdf.

University of Iowa Professor Advises on Disaster Relief Logistics (The Gazette, 11/16/13)

Disasters like the super typhoon that bowled over the Philippines last week, killing thousands and displacing hundreds of thousands, are at the heart of University of Iowa professor Ann Campbell's research.

As a management sciences associate professor with the Tippie College of Business, Campbell studies transportation logistics—specifically focusing on finding more efficient ways to transport relief supplies to disaster zones.

And her research has taught her just how hard it can be to get help to those who most desperately need it.

"It's different when it involves a disaster," Campbell said.

There's a lot more at stake, and a lot more to overcome, according to Campbell. Not only are there traditional logistical hurdles—like finding efficient routes—but transportation, communication, and supply issues can become insurmountable, Campbell said.

Traditional supply chains take years to coordinate and perfect—consider Walmart's efforts to supply its stores with product every day.

"They did not develop that over night," she said. "But when what happened in the Philippines happens, you have to figure it out over night. There is zero in place."

Aid workers have to find suppliers, communicate with recipients, and operate around infrastructure damage to save people desperately needing food, water, and shelter.

"Speed is a big issue," she said

And so is communication, with disasters like Typhoon Haiyan wiping out cellular infrastructure. Campbell said the Philippines' coastal location also makes aid response a challenge—necessitating functioning airports and boats to even get close to the devastated regions.

"Hurricane Katrina was terrible, but at least you could load up an 18-wheeler and start driving down supplies from Iowa," Campbell said. "How do you get supplies to the Philippines when they have one airport serving a lot of people? The complexity and logistics just get worse and worse."

Typhoon Haiyan, which struck the Pacific island nation on Nov. 8, reportedly killed more than 4,400 people, injured more than 12,100 people, displaced more than 900,000 and left more than 1,000 missing. A week after the deadly typhoon, international responders on Friday still were battling clogged airports, blocked roads, and lack of manpower, according to news reports.

Campbell, through her research, said there are some new ideas on how to mitigate the complexities that come with getting aid to disaster zones. She is developing a way to use mathematical modeling and high-powered computing to develop quicker and more efficient ways to route vehicles.

The process aims to inform disaster relief drivers on which roads remain usable and which roads are impassable to speed up delivery times. Campbell said it also can be helpful—although not traditionally efficient—to send out multiple trucks at once in hopes of reaching more people quickly.

She said disasters should teach us to prepare and pre-stock supplies—like the American Red Cross does during hurricane season in the United States.

"That's not something every government and every state does," she said. "But you can access things faster if you do that."

Campbell became interested in disaster relief logistics after the Indonesian tsunami in 2004, and her research became increasingly relevant when Hurricane Katrina hit the Gulf Coast in 2005 and again when Haiti was devastated by an earthquake in 2010.

The Haitian disaster taught us, among other things, that disaster aid should be prioritized.

"Haiti had one airport, and they were letting some planes land that didn't have the most important things on them," she said.

Typhoon Haiyan poses its own set of challenges, she said, also highlighting the need to have prepared and flexible leaders.

"The fact that it's only accessible by boat and a few airports makes it so much harder," she said. "You have to have the right people in charge."

Getting Aid to Philippines Is Challenging, Costly (KCRG, 11/13/13)

Don Fields and his wife Sandee, who operate a warehouse for Kids Against Hunger, are preparing to send what they call "medicine in a bag" to victims of Typhoon Haiyan. The warehouse is packed with boxes of food ready to be shipped overseas.

"Each one of these boxes has 36 bags of food, and each bag will feed a family of six for one day," Fields explained.

It's a soy and rice mix that Fields says is formulated to kick-start the digestive system of a person going hungry. But getting this precious cargo—two shipping containers worth—to the Philippines will be costly.

"To ship from my warehouse to the Philippines, a 40-foot container, which is 18 pallets, is $4,000," said Fields.

Which is why many experts say that unless you're part of an organization like Kids Against Hunger, one equipped with the resources to ship large quantities of food or water, the best way to help is to donate money.

Associate Professor Ann Campbell with the University of Iowa said that's because items like boxes are much more expensive to put to good use.

"You have to come up with suppliers, because people were self-sufficient before, in terms of food and water, and you've got to figure out who is going to donate what," said Campbell. "You've got to deal with infrastructure damage."

Campbell, who researches transportation and logistics, says vehicle supply chains are usually set up to maximize profits, but in a disaster, it's no longer about making money.

"Food , shelter, water is priority one," said Campbell. "We're not maximizing profit; it all becomes about speed, and some of the things that maximize profit do not maximize speed."

But Fields is up to the challenge. He'll be communicating with non-government organizations in the Philippines to make sure that this food gets to the people who need it most.

"They have the capability of tracking the food," Fields told us. "We track it all the way over there, we know when it gets to port, we know who unloads it, who picks it up, where it goes, and what warehouse it's stored in, and then people can come in and distribute it."

Mathematics Turned to Helping Disaster Relief After Philippine Typhoon (UPI, 11/12/13)

Mathematics and logistics can improve relief efforts like those under way after Typhoon Haiyan plowed through the Philippines, a U.S. scientist says.

Management sciences Associate Professor Ann Campbell at the University of Iowa is an expert on transportation logistics and has turned to researching more efficient methods for governments, agencies, and businesses to transport relief supplies to disaster areas where roads, ports, and airports are all but destroyed.

Her specialty—vehicle routing—uses mathematical and computer modeling to develop quicker, more efficient ways to move supplies and personnel from one place to another.

Although her research normally is aimed at business supply chains and commercial activities, she says the same research tools can meet the challenge of disaster logistics.

"Commercial supply chains are focused on quality and profitability," Campbell said. "Humanitarian supply chains are focused on minimizing loss of life and suffering, and distribution is focused on equity and fairness much more than in commercial applications."

Campbell's current research is focused on helping drivers in the Philippines learn what roads are still usable and which have become impassable as a result of the disaster, so that emergency workers will know before setting out which path is least likely to be damaged.

"We want to give drivers a recommended path and some back-up options in case they encounter road failures," she said. "In a disaster, it is important to recognize that information on road conditions is slow to come in. Also, cell phones usually don't work, so it is important to give drivers as much information as possible before they leave the depot with supplies."

Thomas Elected to INFORMS Transportation Science and Logistics Society (10/29/13)

Barrett ThomasTippie College of Business Associate Professor Barrett Thomas has been elected vice president/president-elect of the INFORMS Transportation Science and Logistics Society. His three-year term will include a year in the roles of vice president, president, and pastpPresident.

The TSL Society provides INFORMS members with a sustained, specialized focus on the topics of transportation science and logistics. Their efforts are devoted to current and potential problems of the industry, contributions to their solution, and the integration of related branches of knowledge and practice. The INFORMS Transportation Science and Logistics (TSL) Society was formed in 2004 with the merger of the Transportation Science and Logistics Sections and has more than 1,000 members.

Barrett Thomas is as associate professor of management sciences in the Tippie College and also serves as the faculty director of the MBA Strategic Innovation Career Academy. He holds a Leonard A. Hadley Research Fellowship. He is an associate editor of IIE Transactions, Focused Issue on Operations Engineering and Analytics, Transportation Systems Analysis Department; an editorial board member for Surveys in Operations Research and Management Science; and an associate editor of INFOR: Canadian Journal of Operational Research. He recently was awarded an NSF grant (along with Tippie professor Ken Brown) to develop new models to help businesses improve workforce development and employee training. Thomas has a Ph.D. and an M.S. in industrial and operations engineering from the University of Michigan and a B.A. in mathematics and economics from Grinnell College.  

Pant Named Associate Editor of the Year (10/11/13)

Gautam Pant, associate professor of management sciences at the University of Iowa's Tippie College of Business, has been named Associate Editor of the Year for his work with the Information Systems Research (ISR) Journal.

Information Systems Research (ISR) is a journal of INFORMS, the Institute for Operations Research and the Management Sciences. Information Systems Research is a leading international journal of theory, research, and intellectual development, focused on information systems in organizations, institutions, the economy, and society. More information on the journal can be found here: http://isr.journal.informs.org/

Pant has been with the Department of Management Sciences in the Tippie College of Business since 2011. His expertise is in the areas of analytics, business intelligence, online visibility, and web mining. He received the David Eccles Emerging Scholar Award from the University of Utah in 2009-2011. He received his Ph.D. from the University of Iowa in 2004, his M.S. in computer science from Baylor University in 1999 and a B.E. in computer engineering, VJTI, from the University of Mumbai in 1996.

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