An Algorithm of the Integration of Collaborative Filtering Recommendation with Item Category Information
Yu Zhang · Shuxue de shijian yu renshi · 2010
Collaborative Filtering technology has a wide range of research and application in the field of E-commerce.But with the rapid popularization of the Internet and the sharp increase in the size of E-commerce sites,severe sparseness of user ratings make the quality of recommendation system decline.In this paper,an algorithm of the integration of collaborative filtering recommendation with item category information is proposed.And this method combine item category information to select the candidate neighbor set for active user.In the candidate neighbor set,this method considers the scores and categories information of item to predict missing values.Finally,using actual ratings and predicted ratings obtain the active user's neighbors for recommendation.The experimental results show that the algorithm has high accuracy and real-time performance of recommendation.