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
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Automatic personalization based on Web usage mining
Communications of the ACM
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Designing recommender systems for e-commerce: an integration approach
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
Hi-index | 0.00 |
Recommender Systems (RS) help users to make decisions about which product to purchase from the vast amount of products available on the Internet. Currently, many of the existing recommender systems are developed for recommending frequently purchased products where a large amount of explicit ratings data is available to predict user preferences. However, it is difficult to collect this data for products that are infrequently purchased by the users, and, thus, user profiling becomes a major challenge for recommending such products. This paper proposes a recommender system approach that exploits user navigation and product review data for generating user and product profiles, which are used for recommending infrequently purchased products. The evaluation result shows that the proposed approach, named Adaptive Collaborative Filtering (ACF), which utilizes user and product profiles, outperforms the Query Expansion (QE) approach that only utilizes product profiles to recommend products. ACF also performs better than Basic Search (BS) approach, which is widely applied by the current e-commerce applications.