Item-based collaborative filtering recommendation algorithms
Abstract
Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a liveinteraction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amountofavailable information and the number of visitors to Web sites in recentyears poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amountofwork increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between dierent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze dierent item-based recommendation generation algorithms. Welookinto dierenttechniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and dierenttechniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, weexperimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than th...
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Machine learning methods can learn from population and individual user data to automatically adapt the form of interventions to meet the user's needs and capabilities.
“it is also possible to use machine learning methods that can learn from population and individual user data to automatically adapt the form of interventions to meet the user’s needs and capabilities [25-27].”
From The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014) · cites this paper as [26]
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- Authors
- Sarwar, B., Karypis, G., Konstan, J., & Riedl, J.
- Affiliations
- University of Minnesota (US)
- Venue
- Proceedings of the 10th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2001-04
- Pages
- 285-295
- OpenAlex
- W2042281163
- Cited by
- 9,135
- References
- 30
- Citations by year
- 2026: 240 · 2025: 393 · 2024: 484 · 2023: 489 · 2022: 537 · 2021: 673 · 2020: 584 · 2019: 631 · 2018: 568 · 2017: 534 · 2016: 503 · 2015: 505 · 2014: 444 · 2013: 450 · 2012: 414
- FWCI
- 53.50
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- item-based collaborative filtering, recommender systems, personalized recommendation, item-item similarity, cosine similarity, user-item matrix, data sparsity, k-nearest neighbor comparison, large-scale recommendation
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