Recommender system for online dating service

Recommender systems have become extremely common in recent years, and are applied in a variety of applications.

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To help them in their endeavor and to cope with information overload, recommender systems can be utilized.

This thesis introduces reciprocal recommender systems that are aimed towards the domain of online dating.

Recommendation Engines do not take into account the new discovery uncovered by Eastwick and Finkel 2008; also Kurzban and Weeden, 2007; Todd, Penke, Fasolo, and Lenton, 2007 who found that people often report partner preferences that are not compatible with their choices in real life.

a reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores.

In the above example, requires a large amount of information on a user in order to make accurate recommendations.

This is an example of the cold start problem, and is common in collaborative filtering systems.

However, there are also recommender systems for experts, jokes, restaurants, financial services, approaches building a model from a user's past behavior (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users; then use that model to predict items (or ratings for items) that the user may have an interest in.

Users of large online dating sites are confronted with vast numbers of candidates to browse through and communicate with.

hat proportion of unsuitable candidates are paired with the active user.

Since presenting unsuitable candidates can be especially undesirable in this setting, the false positive rate could be the most important factor." page 2945The first recommender system (for the Online Dating Industry) I saw in a paper was:"Recommender System for Online Dating Service (2007)"then I saw the one offered by Intro Analytics.

paper: "Recommender System for Online Dating Service" "User-User and Item-Item collaborative filtering recommenders significantly outperform global algorithms that are currently used by dating sites [offering only Browsing / Searching Options, Powerful Searching Engine but not Compatibility Matching Algorithms]." "A blind experiment with real users [at a proprietary site named Col Fi - exclusively designed for the experiment - where 111 users rated 150 photo-profiles, then two recommendation lists of top 10 profiles were generated] also confirmed that users prefer collaborative filtering based recommendations to global popularity recommendations [of 2 Czech online dating sites: Chcete Me (no longer exists now) and Libim Se Ti]." "Recommendations can be further improved by hybrid algorithms.

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