GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Personalization of Supermarket Product Recommendations
Data Mining and Knowledge Discovery
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combination of Web page recommender systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
On-line personalized sales promotion in electronic commerce
Expert Systems with Applications: An International Journal
Collaborative filtering based on iterative principal component analysis
Expert Systems with Applications: An International Journal
Integrating collaborative filtering and matching-based search for product recommendations
Journal of Theoretical and Applied Electronic Commerce Research
Hi-index | 12.05 |
In the most studies of the past, only purchase data of users were used in e-commerce recommender system, while navigational and behavioral pattern data were not utilized. However, Kim, Yum, Song, and Kim (2005) developed a collaborative filtering technique based on navigational and behavioral patterns of customers in e-commerce sites. In this article, we improve on Kim et al. (2005) methods and further develop a novel recommender system. The proposed system calculates the confidence levels between clicked products, between the products placed in the basket, and between purchased products, respectively, and then the preference level was estimated through the linear combination of the above three confidence levels. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site for compact disc albums. The results from the experimental study clearly showed that the proposed method is superior to Kim et al. (2005) method.