A Collaborative Filtering Recommendation Algorithm Based on Product Clustering

Abstract:

E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

• Periodical: Applied Mechanics and Materials (Volume 267)
• Pages: 87-90
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.267.87
• Online since: December 2012
• Authors: Pu Wang
• Keywords: Collaborative Filtering (CF), K-Mean, Product Clustering, Recommendation Algorithm

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