Collaborative Filtering of Web Service Based on MapReduce
Shihang Huang, Xue Feng Jiang, Nan Zhang, Cheng Zhang, Depeng Dang · 2014
As more and more web services appear on the internet, it becomes more difficult for us to pick out a suitable service among a large number of alternative services. The services recommended by user-based collaborative filtering lack relevance, and it is insufficient to recommend the new services. In this paper, we proposed a collaborative filtering method mixed user-based and item-based collaborative filtering. In order to adapt to the era of big data, it was implemented making use of MapReduce framework. We avoid overestimated similarity and the sparseness to improve the algorithm. Experiment results show that the hybrid collaborative filtering method can not only ensure accuracy, but also provide chance to recommend the new services.