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Recommendation system has been placed much emphasis by researchers and programmers to deal with the information overload. Collaborative filtering algorithm is the most commonly used one. In order to enhance its performance, the Matrix Factorization was discovered to base the collaborative filtering. This paper elaborates on the collaborative filtering algorithm based on Matrix Factorization and gives...
Collaborative filtering is the most worldwide and personalized video recommendation technology. As collaborative filtering recommendation system is often faced with the problem of matrix sparse on user rating. Via the introduction of the concept of collaborative filtering and the analysis of user behaviors and solution to the problem of sparse existing recommendation systems, this paper puts forward...
Dealing with the enormous amount of recruiting information on the Internet, a job seeker always spends hours to find useful ones. To reduce this laborious work, we design and implement a recommendation system for online job-hunting. In this paper, we contrast user-based and item-based collaborative filtering algorithm to choose a better performed one. We also take background information including...
Human behavior can be a direct reflection of their interest and purposes; therefore, we can take advantage of user behavior to predict their interest in the recommendation system. This paper analyzed the relationship between user behavior and interest in the recommender system of personalized video program, and summarized three potentially useful observations: selection, duration, repetition. Implicit...
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