A Hybrid Collaborative Filtering System for Contextual Recommendations in Social Networks
Recommender systems are based mainly on collaborative filtering algorithms, which only use the ratings given by the users to the products. When context is taken into account, there might be difficulties when it comes to making recommendations to users who are placed in a context other than the usual one, since their preferences will not correlate with the preferences of those in the new context. In this paper, a hybrid collaborative filtering model is proposed, which provides recommendations based on the context of the travelling users. A combination of a user-based collaborative filtering method and a semantic-based one has been used. Contextual recommendation may be applied in multiple social networks that are spreading world-wide. The resulting system has been tested over 11870.com, a good example of a social network where context is a primary concern.
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