A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
- 8 October 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Intelligent Systems
- Vol. 22 (5), 68-78
- https://doi.org/10.1109/mis.2007.4338497
Abstract
Collaborative filtering is one of the most widely adopted and successful recommendation approaches. Unlike approaches based on intrinsic consumer and product characteristics, CF characterizes consumers and products implicitly by their previous interactions. The simplest example is to recommend the most popular products to all consumers. Researchers are advancing CF technologies in such areas as algorithm design, human- computer interaction design, consumer incentive analysis, and privacy protection.Keywords
This publication has 8 references indexed in Scilit:
- Item-based top-Nrecommendation algorithmsACM Transactions on Information Systems, 2004
- Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filteringACM Transactions on Information Systems, 2004
- Latent semantic models for collaborative filteringACM Transactions on Information Systems, 2004
- Evaluating collaborative filtering recommender systemsACM Transactions on Information Systems, 2004
- Authoritative sources in a hyperlinked environmentJournal of the ACM, 1999
- The anatomy of a large-scale hypertextual Web search engineComputer Networks and ISDN Systems, 1998
- Recommender systemsCommunications of the ACM, 1997
- GroupLensPublished by Association for Computing Machinery (ACM) ,1994