Modeling the Collaborative User Groups and Their Effectiveness for the Contents Recommender

In this paper, we model the contents recommender which applies the collaborative filtering and the vector comparison techniques. The system mined the users’ usage history about consuming contents, suggested the user favorable contents. We constructed the usage history data set about 49 users for showing the effectiveness of the proposed algorithms, the results were showed that the collaborative filtering technologies are helpful to resolve the data sparseness problems in the contents recommender.

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