Abstract: Recommender systems and recommendation algorithm has become one of the hotspots of data mining research, with the rapid boosting of e-commerce. Support Vector Regression (SVR) algorithm has been introduced to construct a content-based recommend approach. First, the contents of rated items are analyzed with SVR to build regression model of user profiles for active users. Then use the user profiles to give recommendations. Experimental results on the EachMovie dataset shows that the proposed approach has better recommend performance and less time spending than the conventional collaborative filtering approach.

Key words: recommender systems, Support Vector Regression, content-based recommendation