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With its flourishing development of Web 2.0, people not only passively receive the information, but actively share the information with others by web 2.0 technology. Yet, for people, the information overload problem leads difficulty in filtering the explosive information and identifying the needed information. To solve the problem, the recommender systems which make recommendations based on users’ preferences or the contents of items are the widely utilized solutions. However, the interest influence, follow influence and personalized weights of influences may be the important factors for making recommendations. Besides, the related researches do not consider the review influence and the time factor in recommendation. In this work, the novel recommendation method based on two types of influences, including the interest influence and follow influence, and personalized weights for each influence, is proposed for recommending products in a cosmetic-sharing website. The experimental results show the proposed method improves the performance of recommendation.

Font Size: Social Recommendation Based on the Analysis of Interest and Follow Influences