Location-Aware Feature Interaction Learning for Web Service Recommendation

With the increasing prevalence of web services on the World Wide Web, a large number of functionally equivalent web services are provided by different providers. Quality-of-Service (QoS), representing the nonfunctional characteristics, plays an important role in dealing with how to recommend the optimal services to users among these candidates. Many existing methods for predicting QoS values of web services show that QoS values are intensively relevant to location due to the great influence of network distance and the internet connection between users and services. In this paper, we propose a novel location-aware feature interaction learning (LAFIL) method for predicting the QoS values of the user-service matrix and then making the recommendation by learning the underlying relation, which is hidden in the features concerning with location information. LAFIL can effectively solve the problems of data sparsity and cold-start by leveraging the location features of both users and services. To evaluate the performance of our proposed method, comprehensive experiments are conducted using a real-world dataset and the results show that our method achieves better QoS prediction accuracy compared to state-of-the-art approaches.