Rank and relevance in novelty and diversity metrics for recommender systems

RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Volumn , Issue , 2011, Pages 109-116

  Vargas, Saúl ^a   Castells, Pablo ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

diversity; evaluation; metrics; novelty; recommender systems

Indexed keywords

BASIC CONCEPTS; COMMUNITY IS; DIVERSITY; DIVERSITY METRICS; EVALUATION; EVALUATION METRICS; EXPERIMENTAL OBSERVATION; FORMAL FRAMEWORK; METRICS; NOVELTY; STATE OF THE ART;

RECOMMENDER SYSTEMS;

EID: 82555191365     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2043932.2043955     Document Type: Conference Paper

References (22)

1. Adomavicius, G. and Kwon, Y. Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering. In Press.
2. Agrawal, R., Gollapudi, S., Halverson, A., and Ieong, S. Diversifying search results. WSDM 2009, Barcelona, Spain, 5-14.
3. Bellogín, A., Cantador, I., and Castells, P. A Study of Heterogeneity in Recommendations for a Social Music Service. HetRec Workshop at RecSys 2010, Barcelona, Spain, 1-10.
4. Breese, J. S., Heckerman, D., and Kadie, C. Empirical analysis of predictive algorithms for collaborative filtering. UAI 1998, Madison, WI, USA, 43-52.
5. Carbonell, J. G. and Goldstein, J. The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries. SIGIR 1998, Melbourne, Australia, 335-336.
6. Carterette, B. System effectiveness, user models, and user utility: a conceptual framework for investigation. SIGIR 2011, Beijing, China, 903-912.
7. Celma, O. and Herrera, P. A New Approach to Evaluating Novel Recommendations. RecSys 2008, Lausanne, Switzerland, 179-186.
8. Chapelle, O., Metzler, D., Zhang, Y., and Grinspan, P. Expected Reciprocal Rank for Graded Relevance. CIKM 2009, Hong Kong, China, 621-630.
9. Clarke, C. L. A., Kolla, M., Cormack, G. V., Vechtomova, O., Ashkan, A., Büttcher, S., and MacKinnon, I. Novelty and diversity in information retrieval evaluation. SIGIR 2008, Singapore, 659-666.
10. Clarke, C. L. A., Craswell, N., Soboroff, I., and Ashkan, A. A Comparative Analysis of Cascade Measures for Novelty and Diversity. WSDM 2011, Hong-Kong, China, 75-84.
11. Fleder, D. M. and Hosanagar, K. Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity. Management Science 35, 5, 2009, 697-712.
12. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 1, 2004, 5-53.
13. Hu, B., Zhang, Y., Chen, W., Wang, G., and Yang, Q. Characterizing Search Intent Diversity into Click Models. WWW 2011, Hyderabad, India, 17-26.
14. Koren, Y., Bell, R., Volinsky, C. Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42, 8, 2009, 30-37.
15. Lathia, N., Hailes, S., Capra, L., Amatriain, X. Temporal Diversity in Recommender Systems. SIGIR 2010, Geneva, 210-217.
16. McNee, S. M., Riedl, J., and Konstan, J. A. Being Accurate is Not Enough: How Accuracy Metrics have hurt Recommender Systems. CHI 2006, Montréal, Canada, 1097-1101.
17. Moffat, A. and Zobel, J. Rank-biased precision for measurement of retrieval effectiveness. ACM Transactions on Information Systems 27, 1, 2008.
18. Radlinski, F., Kleinberg, R., Joachims, T. Learning diverse rankings with multi-armed bandits. ICML 2008, Helsinki, Finland, 784-791.
19. Vargas, S., Castells, P., and Vallet, D. Intent-Oriented Diversity in Recommender Systems. SIGIR 2011, Beijing, China, 1211-1212.
20. Zhang, M. and Hurley, N. Avoiding Monotony: Improving the Diversity of Recommendation Lists. RecSys 2008, Lausanne, Switzerland, 123-130.
21. Zhou, T., Kuscsik, Z., Liu, J-G., Medo, M., Wakeling, J. R., and Zhang, Y-C. Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. of the National Academy of Sciences of the United States of America 107, 10, 2010, 4511-4515.
22. Ziegler, C-N., McNee, S. M., Konstan, J. A., and Lausen, G. Improving recommendation lists through topic diversification. WWW 2005, Chiba, Japan, 22-32.