An Effective Cold Start Recommendation Method Using Trust and Distrust Networks

• Main Authors: Yu Hau Wan, 萬宇豪
• Other Authors: 陳建錦
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/30278396745354787867

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 99 === Cold start recommendations are important because they help build user loyalty, which is the key to the success of e-services and e-commerce systems. Recommending useful information for new users generally creates a sense of belonging and loyalty, and encourages them to visit e-commerce systems frequently. However, new users require time to become familiar with recommendation systems, so the systems usually have limited information about newcomers and have difficulty providing appropriate recommendations. This so-called cold start phenomenon has a serious impact on the performance of recommendation systems. To address the problem, we propose a cold start recommendation method that integrates trust and distrust networks with a user model to identify trustworthy users. The suggestions of those users are then aggregated to provide useful recommendations for cold start users. Experiments based on the well-known Epinions dataset demonstrate that the proposed method is effective and efficient. Moreover, it outperforms well-known recommendation methods in terms of the coverage rate and execution time, without a significant reduction in the precision of the recommendations.