Context-Aware Web Services Recommendation Based on User Preference

Context-Aware Recommender System aims to recommend items not only similar to those already rated with the highest score, but also that could combine the contextual information with the recommendation process. Existing context-aware Web services recommendation methods directly use context as a \"filter\" to discard services that may conflict with the current user's preference. However, the discarded services may be valuable for another user or for the same user under a new context, as one man's trash may be another's treasure. We assume that failing to handle the contextual reasons behind the user preference may introduce inaccurate recommendation, and even significant biases in recommendation. In this work, we propose a novel method dubbed CASR-UP, which aims to exploit the contextual factors of the user preference to improve Quality of Service (QoS) prediction and services recommendation accuracy. Our method consists of three stages: 1) context-aware similarity mining to get the set of users having similar context with the current user, 2) data filtering based on user preference in current context so as to get the invocation records of the services corresponding to the current user's preference, 3) Web services QoS prediction, recommendation and evaluation by Bayesian Inference. Experimental results on WS-Dream dataset is evaluated by both RMSE and MAE. The results show the proposed method improves prediction accuracy and outperforms the compared methods.