An Incremental Tensor Factorization Approach for Web Service Recommendation

Wancai Zhang, Hailong Sun, Xudong Liu, Xiaohui Guo

Abstract: With the development of Service-Oriented technologies, the amount of Web services grows rapidly. QoS-Aware Web service recommendation can help service users to design more efficient service-oriented systems. However, existing methods assume the QoS information for service users are all known and accurate, but in real case, there are always many missing QoS values in history records, which increase the difficulty of the missing QoS value prediction. By considering the user-service-time three dimension context information, we study a Temporal QoS-Aware Web Service Recommendation Framework which aims to recommend best candidates to service user's requirements and meanwhile improve the QoS prediction accuracy. We propose to envision such QoS value data as a tensor which is known as multi-way array, and formalize this problem as a tensor factorization model and propose a Tucker decomposition (TD)algorithm which is able to deal with the triadic relations of user-service-time model. However, one major challenge is that how to deal with the dynamic incoming service QoS value data streams. Thus, we introduce the incremental tensor factorization (ITF)method which is (a) scalable, (b) space efficient (it does not need to store the past data). Extensive experiments are conductedbasedc on our real-world QoS dataset collected on Planet-Lab, comprised of service invocation response-time values from 408 users on 5,473 Web services at 240 time periods. Comprehensive empirical studies demonstrate that our approach is faster and more accuracy than other approaches.

External IDs:dblp:conf/icdm/ZhangSLG14