GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Self-Organizing Maps
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
e-learning experience using recommender systems
Proceedings of the 42nd ACM technical symposium on Computer science education
A topic-based recommender system for electronic marketplace platforms
Expert Systems with Applications: An International Journal
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Recommendation system is one of the most important techniques in some E-commerce systems such as virtual shopping mall. With the prosperity of E-commerce, more and more people are willing to perform Internet shopping, which resulted in an overwhelming array of products. Traditional similarity measure methods make the quality of recommendation system decreased dramatically in this situation. To address this issue, we present a novel method that combines the clustering which is based on apriori-knowledge and content-based technique to calculate the customer’s nearest neighbor, and then provide the most appropriate products to meet his/her needs. Experimental results show efficiency of our method.