Collaborative filtering recommendation algorithm based on user characteristics and item attributes
Zhiqiang Li · Journal of Computer Applications · 2011
Under the extremely sparse data environment,the traditional collaborative filtering algorithms only depenging on users rating data cannot achieve satisfactory recommended quality.A recommendation algorithm based on user characteristics and item attributes was provided.First,the time-related interest degree was introduced in the process of user similarity calculation,which made a more accurate nearest neighbor set.While predicting the rating for the target user,the trust measure was used to reflect the neighbors' contribution level for the ultimate recommendation.In addition,the users' preference on item attribute instead of rating score was used to recommend the new items.The experimental results based on MovieLens data set show that the improved algorithm can solve the problem of cold-start and improve the accuracy of system recommendation significantly.