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, as new users take time to become familiar with recommendation systems, the systems usually have limited information about newcomers and have difficulty providing appropriate recommendations. The 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 a web of trust 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, and outperforms well-known recommendation methods by a significant margin.


Recommendation Systems, Collaborative Filtering, Trust Network


ISBN: [978-1-86435-644-1]; Full paper