| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 101 === 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, there is the information overload problem to filter the explosive information and find what people want hard. To solve the problem, the recommendation systems such as based on users’ preferences or the contents of items are the widely utilized solution. However, the interest influence, follow influence and personalized weights of influences may be the important factor for recommendation. Besides, the related researches do not consider the review influence and the time factor in recommendation.
In our work, we proposed the novel recommendation method base on two types of influences including the interest influence and follow influence, and personalized weights for each influence for recommending products in cosmetic-sharing website, Urcosme. The experimental results show our proposed methods improve the performance of recommendation.
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