Computer scientists at the University of Massachusetts Lowell have studied the behavior of 200,000 people on Chinese dating site Baihe.com to gain insight into how successful matches are made.
Although predictable in many ways, people also break their own stated dating requirements. The researchers found 70 percent of communications from women and 55 percent from men were sent to people who did not meet their stated preferences.
Machine learning helped the researchers determine with 75-percent accuracy whether a user would reply to another's initial contact message. Their methods factored in characteristics such as age, height, location, income, and education level as well as activity, popularity on the site, and compatibility in preferences and attractiveness.
Unlike a book recommendation, which is a single directional process, dating sites must match users who have a mutual interest. The researchers used collaborative-filtering algorithms to develop a reciprocal recommendation system to match users of mutual interest. Collaborative filtering-style algorithms based on prior communications of the user and those with similar interests and attractiveness proved significantly more successful than content-based algorithms that rely on factors such as age and income.
Because users often make dating choices that differ from their reported preferences, the collaborative-filtering algorithm that factors in actual user behavior is an effective matching tool.
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