A Recommendation System for Online Social Feeds by Exploiting User Response Behaviour

• Main Authors: Ping-Han Soh, 蘇評翰
• Other Authors: Ming-Syan Chen
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/14542865536511655923

碩士 === 國立臺灣大學 === 電信工程學研究所 === 100 === In recent years, online social networks have been dramatically expanded. Active users spend hours communicating with each other via these networks such that an enormous amount of data is created every second. The tremendous amount of newly created information costs users much time to discover interesting messages from their online social feeds. The problem is even exacerbated if the users access these networks via mobile devices. To help users discover interesting messages efficiently, in this paper, we propose a new approach to recommend interesting messages for each user by exploiting the user''s response behaviour. The proposed approach is then demonstrated to be easily extended to deal with the temporal recommendation. We investigate the response behaviour on the most popular social network, and the experimental results show that the proposed approach provides obvious improvement over the current online social feeds.