Time-Aware Web Service Recommendations Using Implicit Feedback

With the rapid development of SOA (Service Oriented Architecture), an increasing number of Web services have been published on the Internet. How to recommend suitable Web services to users becomes a challenging problem. Existing Web services recommendation approaches based on collaborative filtering mainly focus on QoS (Quality of Service) prediction. Recommending services based on users´ ratings on services are seldom reported since such explicit feedback data is difficult to collect. In this paper, we report a dataset of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. In addition, time is becoming an increasingly important factor in recommenders since time effects influence users´ preferences to a large extent. Based on the collected dataset, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.