An Electronic Commerce Recommender System Based on Product Character

Abstract:

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

• Periodical: Advanced Materials Research (Volume 267)
• Pages: 909-912
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.267.909
• Online since: June 2011
• Authors: Shen Bao Chen
• Keywords: Collaborative Filtering (CF), Electronic Commerce, Personalized, Product Character, Recommender System

References

[1] Songjie Gong, An Efficient Collaborative Recommendation Algorithm Based on Item Clustering, Lecture notes in electrical engineering, Volume 72, pp: 381-387.

DOI: 10.1007/978-3-642-14350-2_48

[2] S.H.S. Chee，J Han，K. Wang．Rectree: An efficient collaborative filtering method. Lecture Notes in Computer Science, 2114, (2001).

[3] Songjie Gong, Employing User Attribute and Item Attribute to Enhance the Collaborative Filtering Recommendation, Journal of Software, Volume 4, Number 8, October 2009, pp: 883-890.

DOI: 10.4304/jsw.4.8.883-890

[4] L. H. Ungar and D. P. Foster. A Formal Statistical Approach to Collaborative Filtering. Proceedings of Conference on Automated Leading and Discovery (CONALD), (1998).

[5] B. Sarwar, G. Karypis, J. Konstan and J. Riedl, Recommender systems for large-scale e-commerce: Scalableneighborhood formation using clustering, Proceedings of the Fifth International Conference on Computer andInformation Technology, (2002).

[6] Songjie Gong, A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering, Journal of Software, Volume 5, Number 7, July 2010, pp: 745-752.

DOI: 10.4304/jsw.5.7.745-752

[7] Songjie Gong, Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering, Applied Mechanics and Materials, Volume 39, pp: 540-544.

DOI: 10.4028/www.scientific.net/amm.39.540

[8] Songjie Gong, An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms, Advanced Materials Research, Volume 159, pp: 671-675.

DOI: 10.4028/www.scientific.net/amr.159.671

[9] George, T., & Merugu, S. A scalable collaborative filtering framework based on co-clustering. In Proceedings of the IEEE ICDM Conference. (2005).

DOI: 10.1109/icdm.2005.14

[10] M. O. Conner and J. Herlocker. Clustering Items for Collaborative Filtering. In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, August (1999).

[11] A. Kohrs and B. Merialdo. Clustering for Collaborative Filtering Applications. In Proceedings of CIMCA'99. IOS Press, (1999).

[12] Lee, WS. Online clustering for collaborative filtering. School of Computing Technical Report TRA8/00. (2000).