Collaborative Filtering Recommendation Model Considering Integration of User Rating and Attribute Similarity
Abstract: Directed at the
problem that the collaborative filtering algorithm tends to be subject to data sparsity
and cold boot, a collaborative filtering recommendation algorithm based on
improvement nearest neighbor is proposed. Firstly, the current user’s k nearest
neighbor and reverse k nearest neighbor are obtained on the basis of the
similarity algorithm, which are used to compute positive and negative credibility
values respectively based on their predicted ratings and the current user
ratings. Then modifications of constraint are made for the users who are both
the k nearest neighbor and the reverse k nearest neighbor and the hot
resources. Finally, the collaborative filtering recommendation algorithm based
on weight fusion is derived and a comparative experiment of simulation is
conducted on MovieLens. The result shows that the algorithm in the Thesis
decreases the mean absolute error value while improving the accuracy of
recommendation.
Author: Tian Jiule, Xu Hang
Journal Code: jptkomputergg160120
