The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Rank and relevance in novelty and diversity metrics for recommender systems
Open accessAbstract
The Recommender Systems community is paying increasing attention to novelty and diversity as key qualities beyond accuracy in real recommendation scenarios. Despite the raise of interest and work on the topic in recent years, we find that a clear common methodological and conceptual ground for the evaluation of these dimensions is still to be consolidated. Different evaluation metrics have been reported in the literature but the precise relation, distinction or equivalence between them has not been explicitly studied. Furthermore, the metrics reported so far miss important properties such as taking into consideration the ranking of recommended items, or whether items are relevant or not, when assessing the novelty and diversity of recommendations.
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Cites
Matrix Factorization Techniques for Recommender Systems
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Empirical Analysis of Predictive Algorithms for Collaborative Filtering
Breese, J. S., Heckerman, D. E., & Kadie, C. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.7363 · Cited by 4,772
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Carbonell, J., & Goldstein, J. (1998) · Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/290941.291025 · Cited by 2,329
The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries
Carbonell, J., & Goldstein, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130369 · Cited by 2,156
Improving recommendation lists through topic diversification
Ziegler, C.-N., McNee, S. M., Konstan, J. A., & Lausen, G. (2005) · Proceedings of the 14th international conference on World Wide Web - WWW '05 · doi:10.1145/1060745.1060754 · Cited by 1,892
Being accurate is not enough: how accuracy metrics have hurt recommender systems
McNee, S. M., Riedl, J., & Konstan, J. A. (2006) · CHI '06 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1125451.1125659 · Cited by 1,066
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Solving the apparent diversity-accuracy dilemma of recommender systems
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000488107 · Cited by 972
Novelty and diversity in information retrieval evaluation
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Cited by
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News recommender systems – Survey and roads ahead
Karimi, M., Jannach, D., & Jugovac, M. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.04.008 · Cited by 336
Multistakeholder recommendation: Survey and research directions
Abdollahpouri, H., Adomavicius, G., Burke, R., Guy, I., Jannach, D., Kamishima, T., Krasnodebski, J., & Pizzato, L. (2020) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09256-1 · Cited by 319
Diversity in Big Data: A Review
Drosou, M., Jagadish, H. V., Pitoura, E., & Stoyanovich, J. (2017) · Big Data · doi:10.1089/big.2016.0054 · Cited by 140
The Effect of Recommendations on Network Structure
Su, J., Sharma, A., & Goel, S. (2016) · Proceedings of the 25th International Conference on World Wide Web · doi:10.1145/2872427.2883040 · Cited by 78
Metadata
- Authors
- Vargas, S., & Castells, P.
- Venue
- Proceedings of the fifth ACM conference on Recommender systems
- Publisher
- ACM
- Published
- 2011-10-23
- Pages
- 109-116
- OpenAlex
- W2144807535
- Cited by
- 709
- References
- 23
- FWCI
- 23.56
- Type
- conference paper
- Language
- en
- Open access
- green
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