Hybrid Recommendation Algorithm for E-Commerce Website
Peng-yu Lu, Xiaoxiao Wu, De-ning Teng · 2015
Traditional recommendation algorithms face some serious problems, including data sparsity, cold start and inefficiency. To better address the problems above, the paper proposes a hybrid recommendation algorithm based on improved collaborative filtering of user context fuzzy clustering and content-based. For collaborative filtering, firstly, user classification is based on fuzzy clustering according to user context, and then collaborative filtering is used to recommend products for similar users. And the improved content-based algorithm sets up feature vectors for users and items dynamically. Experiments show that the hybrid algorithm can avoid defects of single algorithm and improve the performance in both recommendation quality and efficiency, which opens up exciting avenues for future research.