Social-based Adjustment Recommender System－ Increasing Recommendation Accuracy by Combining Friends’ Opinions with User-based Collaborative Filtering

• Main Authors: Yu-Rung Shiue, 薛鈺蓉
• Other Authors: Seng-Cho Chou
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/87618286491567452426

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 98 === Recommender system has been studied for many years. The basic types of recommender system are: content-based, collaborative filtering, and hybrid approach. Recently, with the prosperity of social network, many studies begin to notice that the recommendation accuracy can be enhanced by taking social influence such as trust mechanism into consideration, especially when there is a cold start problem. For collaborative filtering recommender system, the source to construct user profile comes from rating information based on user''s personal preference. The items we recommend to a person are based on the similarity between preferences of users. As for trust mechanism, it is assumed that the similarity between users'' preferences positively correlate with their trust relationship. In sum, the main idea is focus on finding a recommender support which has positive relative with users’ preferences. Therefore, our work aims to enhanced recommendation accuracy by increasing the source of users’ preferences that system can directly get. Our study tries to use social network’s friends-making mechanism to collect user’s interest through user’s friends’ active opinions. Recommender system can analyze user’s preference not only from user’s ratings, but also from friends’ active recommendations. So we propose social-based adjustment recommender system (SARS) algorithm, which merged the counts of friends’ active recommendations into the score predicted by user-based collaborative filtering as final predict score. According to experiment result, we prove that recommendation accuracy can be improved by considering friends’ active opinions.