Dynamic Similarity Collaborative Filtering of Recommendation System

• Main Author: 林凡鈞
• Other Authors: 李素瑛
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/20475132382030614965

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 101 === In the researches of Recommendation System, Collaborative Filtering is one of the most effective approaches. With high accuracy in recommendations, however, few researches focus on Dynamic Collaborative Filtering which considers the time influence in Collaborative Filtering. This causes the recommendations inappropriate because the system might make a recommendation which is out of date. On the other hand, most of the existing dynamic Collaborative Filtering works are focused on Dynamic Weight. Dynamic Weight Collaborative Filtering uses decay ratings to achieve dynamic property. In other words, the rating might be multiplied by a decay weight according to the rating time. The older the rating is, the lower the rating becomes. Nevertheless, rating decay can also be interpreted as the changes of users’ favor. We believe that people would not actually change their perceptions on the same item because of time. Hence, we propose a different way in Dynamic Collaborative Filtering called Dynamic Similarity Collaborative Filtering (DSCF). The similarities among users are decayed rather than the ratings. In our opinion, we suppose that time might change the similarities among people. We also propose an enhanced method of DSCF. We feedback the predicted rating via actual value in order to obtain a more appropriate similarity decay rate. The experimental results demonstrate the proposed method has higher accuracy and less computation.