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Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
Abstract
Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating data sets and different rating prediction algorithms.
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Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Adomavicius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,310
Self-citationItem-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
Evaluating collaborative filtering recommender systems
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963772 · Cited by 5,798
GroupLens: an open architecture for collaborative filtering of netnews
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994) · Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW '94 · doi:10.1145/192844.192905 · Cited by 5,058
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
On the measurement of inequality
Atkinson, A. B. (1970) · Journal of Economic Theory · doi:10.1016/0022-0531(70)90039-6 · Cited by 3,945
Fab: content-based, collaborative recommendation
Balabanović, M., & Shoham, Y. (1997) · Communications of the ACM · doi:10.1145/245108.245124 · Cited by 2,934
Singular value decomposition and least squares solutions
Golub, G. H., & Reinsch, C. (1970) · Numerische Mathematik · doi:10.1007/bf02163027 · Cited by 2,929
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
Analysis of recommendation algorithms for e-commerce
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000) · Proceedings of the 2nd ACM conference on Electronic commerce · doi:10.1145/352871.352887 · Cited by 1,827
Measurement of Inequality of Incomes
Gini, C. (1921) · The Economic Journal · doi:10.2307/2223319 · Cited by 1,710
The singular value decomposition: Its computation and some applications
Klema, V., & Laub, A. (1980) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1980.1102314 · Cited by 1,631
Application of Dimensionality Reduction in Recommender System - A Case Study
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000) · doi:10.21236/ada439541 · Cited by 1,491
Collaborative filtering with temporal dynamics
Koren, Y. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557072 · Cited by 1,203
Brynjolfsson, E., Hu, Y. (., & Smith, M. D. (2003) · Management Science · doi:10.1287/mnsc.49.11.1580.20580 · Cited by 1,173
Bennett, J. R., & Lanning, S. (2007) · Cited by 1,134
THE PROBABILITY RANKING PRINCIPLE IN IR
ROBERTSON, S. E. (1977) · Journal of Documentation · doi:10.1108/eb026647 · Cited by 1,103
Readings in information retrieval
Jones, K. S., & Willett, P. (1997) · Morgan Kaufmann Publishers Inc. eBooks · Cited by 1,069
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
Learning Collaborative Information Filters
Billsus, D., & Pazzani, M. J. (1998) · Cited by 1,040
Brynjolfsson, E., Hu, Y. (., & Simester, D. (2011) · Management Science · doi:10.1287/mnsc.1110.1371 · Cited by 847
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
Fleder, D., & Hosanagar, K. (2009) · Management Science · doi:10.1287/mnsc.1080.0974 · Cited by 745
Weighted low-rank approximations
Srebro, N., & Jaakkola, T. (2003) · International Conference on Machine Learning · Cited by 715
Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval
Zhai, C., Cohen, W. W., & Lafferty, J. (2015) · ACM SIGIR Forum · doi:10.1145/2795403.2795405 · Cited by 558
Avoiding monotony: improving the diversity of recommendation lists
Zhang, M., & Hurley, N. (2008) · Proceedings of the 2008 ACM conference on Recommender systems · doi:10.1145/1454008.1454030 · Cited by 456
Smyth, B., & McClave, P. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44593-5_25 · Cited by 422
(1950) · Medical Entomology and Zoology · Cited by 396
Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Zhai, C. X., Cohen, W. W., & Lafferty, J. (2003) · Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval · doi:10.1145/860435.860440 · Cited by 366
Collaborative filtering via gaussian probabilistic latent semantic analysis
Hofmann, T. (2003) · Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval · doi:10.1145/860435.860483 · Cited by 362
Designing novel review ranking systems: predicting the usefulness and impact of reviews
Ghose, A., & Ipeirotis, P. G. (2007) · Proceedings of the ninth international conference on Electronic commerce · doi:10.1145/1282100.1282158 · Cited by 322
Lower Rank Approximation of Matrices by Least Squares With Any Choice of Weights
Gabriel, K. R., & Zamir, S. (1979) · Technometrics · doi:10.1080/00401706.1979.10489819 · Cited by 295
The BellKor 2008 Solution to the Netflix Prize
Bell, R. M., Koren, Y., & Volinsky, C. T. (2007) · Cited by 188
McSherry, D. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46119-1_17 · Cited by 145
Collaborative Filtering Using Weighted Majority Prediction Algorithms
Nakamura, A., & Abe, N. (1998) · International Conference on Machine Learning · Cited by 144
Using Singular Value Decomposition Approximation for Collaborative Filtering
Zhang, S., Wang, W., Ford, J., et al. (2006) · doi:10.1109/icect.2005.102 · Cited by 120
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
Su, X., & Khoshgoftaar, T. M. (2006) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2006.41 · Cited by 116
Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety
Brynjolfsson, E., Smith, M. D., & Hu, Y. J. (2018) · doi:10.1184/r1/6471140 · Cited by 104
Design of a shopbot and recommender system for bundle purchases
Garfinkel, R., Gopal, R. D., Tripathi, A., & Yin, F. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.05.005 · Cited by 92
Collaborative Filtering via Ensembles of Matrix Factorizations
Wu, M. (2007) · Max Planck Institute for Plasma Physics · Cited by 82
Applying collaborative filtering techniques to movie search for better ranking and browsing
Park, S.-T., & Pennock, D. M. (2007) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1281192.1281252 · Cited by 81
Zheng, Z., & Padmanabhan, B. (2006) · Management Science · doi:10.1287/mnsc.1050.0488 · Cited by 65
Selectively acquiring ratings for product recommendation
Huang, Z. (2007) · International Conference on Electronic Commerce · doi:10.1145/1282100.1282171 · Cited by 29
Using external aggregate ratings for improving individual recommendations
Umyarov, A., & Tuzhilin, A. (2011) · ACM Transactions on the Web · doi:10.1145/1921591.1921594 · Cited by 27
What Else Is There? Search Diversity Examined
Sanderson, M., Tang, J., Arni, T., & Clough, P. D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00958-7_51 · Cited by 26
Diversity in open social networks
Daniel Lemire, A., Downes, S., & Paquet, S. (2008) · Cited by 12
Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Rank and relevance in novelty and diversity metrics for recommender systems
Vargas, S., & Castells, P. (2011) · Proceedings of the fifth ACM conference on Recommender systems · doi:10.1145/2043932.2043955 · Cited by 709
DRN: A Deep Reinforcement Learning Framework for News Recommendation
Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N. J., Xie, X., & Li, Z. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3185994 · Cited by 630
Luo, X., Zhou, M., Xia, Y., & Zhu, Q. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2308433 · Cited by 629
Kaminskas, M., & Bridge, D. (2017) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2926720 · Cited by 465
Metadata
- Authors
- Adomavicius, G., & Kwon, Y.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2012-05
- Volume
- 24
- Issue
- 5
- Pages
- 896-911
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2153578526
- Cited by
- 675
- References
- 48
- FWCI
- 96.18
- Type
- journal article
- Language
- en
- Open access
- closed
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