Evaluation of item-based top-N recommendation algorithms

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2001, Pages 247-254

  Karypis, George ^a  

^a University of Minnesota   (United States)

Author keywords

Collaborative filtering; Recommender systems

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; ELECTRONIC COMMERCE; MATRIX ALGEBRA; PROBABILITY; TELEVISION;

RECOMMENDATION ALGORITHMS;

WORLD WIDE WEB;

EID: 0035747556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502624.502627     Document Type: Conference Paper

References (21)

1. FAB: Content-based collaborative recommendation
2. Recommendation as classification: Using social and content-based information in recommendation
3. Agglomerative clustering of a search engine query log
4. Learning collaborative information filters
5. A non-invasive learning approach to building web user profiles
6. Combining collaborative filtering with personal agents for better recommendations
7. Recommending and evaluating choices in a virtual community of use
8. Cross-sell: A fast promotion-tunable customer-item recommendation method based on conditional independent probabilities
9. GroupLens: Applying collaborative filtering to Usenet news
10. Discovery of aggregate usage profiles for web personalization
11. Recommender systems
12. GroupLens: An open architecture for collaborative filtering of netnews
13. Empirical analysis of predictive algorithms for collaborative filtering
14. Analysis of recommendation algorithms for E-Commerce
15. Application of dimensionality reduction in recommender systems - A case study
16. Recommender systems in e-commerce
17. Social information filtering: Algorithms for automating "word of mouth"
18. PHOAKS: A system for sharing recommendations
19. Clustering methods for collaborative filtering
20. Horting hatches and egg: A new graph-theoretic approach to collaborative filtering