PROJECT TITLE :
Time Aware and Data Sparsity Tolerant Web Service Recommendation Based on Improved Collaborative Filtering
With the incessant growth of web services on the Internet, how to style effective web service recommendation technologies based mostly on Quality of Service (QoS) is becoming more and a lot of important. Web service recommendation can relieve users from robust work on service choice and improve the potency of developing service-oriented applications. Neighborhood-based collaborative filtering has been widely used for net service recommendation, in that similarity measurement and QoS prediction are 2 key problems. However, traditional similarity models and QoS prediction ways rarely consider the influence of time info, which is an important issue affecting the QoS performance of web services. Furthermore, it's difficult for the prevailing similarity models to capture the actual relationships between users or services thanks to knowledge sparsity. The two shortcomings seriously devalue the performance of neighborhood-based mostly collaborative filtering. In this paper, the authors propose an improved time-aware collaborative filtering approach for top-quality internet service recommendation. Our approach integrates time information into each similarity measurement and QoS prediction. Additionally, so as to alleviate the data sparsity downside, a hybrid personalised random walk algorithm is designed to infer indirect user similarities and service similarities. Finally, a series of experiments are provided to validate the effectiveness of our approach.
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