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Research and Improvement of Personalized Recommendation Algorithm Based on Collaborative Filtering


Lijuan Zheng, Yaling Wang, Jiangang Qi, Dan Liu


Vol. 7  No. 7  pp. 134-138


Collaborative filtering is one of the most frequently used techniques in personalized recommendation systems. But currently used user-based collaborative filtering recommendation algorithm and the collaborative filtering recommendation algorithm based on item rating prediction has disadvantage in similarity computation method. Basing on this disadvantage, the paper puts forward an improved collaborative filtering recommendation algorithm. We improve it in two aspects: First, we bring in a coefficient to coordinate the problem of inexact finding and falling recommendation quality which is caused by the fewer items when weighting the user similarity. Second, we collect the users’ interest words implicitly when build the user interest model. At last, we develop an online network bookshop as an example, test and analyze the three algorithms. The testing results show that in most cases, the improved algorithm that we put forward can improve recommendation quality.


Collaborative Filtering, Personalized Recommendation Algorithm, Mean Absolute Error