Humans and machines both succeed at tasks where they have readily available or proximal information.
How Cupid is Counting on Data Science to Find the Perfect Match
We need a clear label associated with our KPI, and the information needs to be as clean as possible, with no noise to distract from what it is trying to say. In those tasks where information is obscured or distal, both humans and machine learning find it far more difficult to make sensible decisions.
Compatibility requires solving the right problem using the right information. Dating focuses on escalation rather than long term success. Escalation is initially based on the proximal or nearby - location, appearance, and trivial social interaction. This is necessary during the onset period of a relationship, but they are not strong predictors of success.
Compatibility over a long period is based on things like career and family goals, how personalities mesh, how you approach key choices, how you communicate, and so forth.
The site collects both demographic data age, gender, location , psychographic data likes, interests and habits and behavioral data actions taken on the site through its vast surveys. The problem that data scientists at dating agencies are different from those at the likes of Netflix and Amazon are trying to solve when they deploy machine learnings for recommendation engines.
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Where with Netflix, you just need someone to like a movie, in the case of dating you essentially need the movie to like you back. So you have both female agreeableness and male agreeable, and female satisfaction and male satisfaction. You have the actor effects and the partner effects.
Multiple data sources enable richer dating profiles
While they are notoriously secretive when it comes to their algorithm, researchers at Cornell University have been able to identify the elements considered in producing a match. The algorithm evaluates each new user in six areas — 1 level of agreeableness, 2 preference for closeness with a partner, 3 degree of sexual and romantic passion, 4 level of extroversion and openness to new experience, 5 how important spirituality is, and 6 how optimistic and happy they are.
A better chance of a good match is usually directly proportional to a high similarity in these areas. They then apply a bipartite matching approach, where every man is matched to several women, and vice versa. The algorithm runs daily, and the pool of eligible candidates for each user changes in the same time frame, with previous matches taken out and location changes accounted for. Psychologists have been trying to identify the causes of a successful marriage for hundreds of years. The idea that machines - cold, loveless machines - could do it for us is not without irony, but in the future these algorithms could be key to stopping us go the way of the panda, sitting in cages unwilling to reproduce.
Popular Recent Top Authors. A - It is about the opportunity to do better prediction. With larger-scale data from more sources on how people behave in a network context becoming available, there are a lot of opportunities to apply ML algorithms to discover patterns on how people behave and predict what will happen next. It is also possible to derive new social science theories from dynamic data through computational studies.
Besides, the education component is also exciting as industry needs a workforce with data analytics skills. That's also why we at the University of Iowa have started a bachelor's program in Business Analytics and plan to roll out a Master's program in this area as well.
Big Dating: It's a (Data) Science | HuffPost
A - I want to better understand and predict social networks dynamics at different scales. For example, dyadic link formation at the microscopic level, the flow of information and influence at the mesoscopic level, as well as how network topologies affect network performance at the macroscopic level. Q - What Machine Learning methods have you found most helpful?
A - It really depends on the context and it is hard to find a silver bullet for all situations. I usually try several methods and settle with the one with the best performance. As for conferences, I found the following helpful for my own research: Improving our ability to make predictions is definitely very compelling!
5.3 Big Data Analytics for Online Dating Services
Now, let's discuss how this applies in some of your research Q - Your recent work on developing a "Netflix style" algorithm for dating sites has received a lot of press coverage A - We try to address user recommendation for the unique situation of reciprocal and bipartite social networks e. The idea is to recommend dating partners who a user will like and will like the user back.
In other words, a recommended partner should match a user's taste, as well as attractiveness. Q - How did Machine Learning help? A - In short, we extended the classic collaborative filtering technique commonly used in item recommendation for Amazon. A - People's behaviors in approaching and responding to others can provide valuable information about their taste, attractiveness, and unattractiveness.
Our method can capture these characteristics in selecting dating partners and make better recommendations. Editor Note - If you are interested in more detail behind the approach, both Forbes' recent article and a feature in the MIT Technology Review are very insightful. Here are a few highlights:. Recommendation Engine from MIT Tech Review - These guys have built a recommendation engine that not only assesses your tastes but also measures your attractiveness. It then uses this information to recommend potential dates most likely to reply, should you initiate contact.
The dating equivalent [of the Netflix model] is to analyze the partners you have chosen to send messages to, then to find other boys or girls with a similar taste and recommend potential dates that they've contacted but who you haven't. In other words, the recommendations are of the form: The problem with this approach is that it takes no account of your attractiveness. If the people you contact never reply, then these recommendations are of little use. So Zhao and co add another dimension to their recommendation engine. They also analyze the replies you receive and use this to evaluate your attractiveness or unattractiveness.
Obviously boys and girls who receive more replies are more attractive. When it takes this into account, it can recommend potential dates who not only match your taste but ones who are more likely to think you attractive and therefore to reply. Machine Learning from Forbes - "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 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.
Finally, for more technical details, the full paper can be found here. A - We want to further improve the method with different datasets from either dating or other reciprocal and bipartite social networks, such as job seeking and college admission. How to effectively integrate users' personal profiles into recommendation to avoid cold start problems without hurting the method's generalizability is also an interesting question we want to address in future research.
That all sounds great - good luck with the next steps! Here we directly measure one's influence, i.
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