Computer Science ›› 2017, Vol. 44 ›› Issue (9): 250-255.doi: 10.11896/j.issn.1002-137X.2017.09.047

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Parallel Collaborative Filtering Algorithm Based on User Recommended Influence

WANG Shuo, SUN Guang-ming, ZOU Jing-zhao and LI Wei-sheng   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The similarity based on common scores and full item sets has failed to identify the nearest neighbor recommendation influence,which brings about lower recommend quality and poor scalability.Through non-common rating items,common score item categories and user visited times,this paper proposed a parallel collaborative filtering algorithm based on user recommendation influence.It computes the user recommended novelty degree and interest coincidence to measure user recommendation influence ability.By adding it to calculate similarity,the algorithm can effectively restrain the highly recommended users with high similarity,avoid similarity computation on full item sets and improve the quality of recommendation. Further more,by using MapReduce parallelization,this algorithm has good real-time performance and scalability.The experimental results show that the parallel algorithm is of higher recommendation quality and better scalability on big data.

Key words: Recommendation influence degree,Recommendation novelty degree,Interest coincidence degree,MapReduce paralleliation

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