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30 Jan

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.

Initial data analysis highlights the problem of over-recommending popular users, a standard problem for collaborative filtering applied to product recommendation, but more acute in people-to-people recommendation.

We address this problem with a two-stage recommender process that employs a Decision Tree derived from interactions data as a “critic” to re-rank candidates generated by collaborative filtering.

The differences between collaborative and content-based filtering can be demonstrated by comparing two popular music recommender systems – and Pandora Radio.

Each type of system has its own strengths and weaknesses.

Recommender systems have become increasingly popular in recent years, and are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

(2010) Interaction-Based Collaborative Filtering Methods for Recommendation in Online Dating. (eds) Web Information Systems Engineering – WISE 2010. The main problem to be solved is that matches must be highly personalized.

We propose a number of new methods and metrics to measure and predict potential improvement in user interaction success, which may lead to increased user satisfaction with the dating site.

We use these metrics to rigorously evaluate the proposed methods on historical data collected from a commercial online dating web site.

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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.