| Summary: | 碩士 === 國立交通大學 === 資訊管理研究所 === 100 === With the rapid development of web 2.0, users not only read the information shared by others but also generate content by themselves. Among the applications of web 2.0, social networking websites continue to proliferate and the volume of content keep growing, so that information overload problem makes users have difficulty in choosing useful and relevant information. To resolve such problem in social media, most of researches only utilize users’ preference, the content of items or social influence to make recommendations. However, people’s preferences towards items may be affected by three decision factors including social friends, personal interest and item popularity. Moreover, each decision factor has different impact on each user.
In this work, we propose a novel recommendation method based on different aspects of influences, including social, interest and popularity, and personalized tendency towards these three decision factors to recommend photos in a photo-sharing website- Flickr. Because these influences have different degree of impact on each user, the personalized tendencies towards these three influences are regarded as personalized weights to combine the influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the prediction accuracy and the quality of recommendation.
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