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Feature weighting in content based recommendation system using social network analysis
Abstract
We propose a hybridization of collaborative filtering and content based recommendation system. Attributes used for content based recommendations are assigned weights depending on their importance to users. The weight values are estimated from a set of linear regression equations obtained from a social network graph which captures human judgment about similarity of items.
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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 [25]
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Metadata
- Authors
- Debnath, S., Ganguly, N., & Mitra, P.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 1041-1042
- OpenAlex
- W2096235681
- Cited by
- 253
- References
- 3
- Citations by year
- 2025: 1 · 2024: 7 · 2023: 11 · 2022: 8 · 2021: 7 · 2020: 14 · 2019: 12 · 2018: 33 · 2017: 23 · 2016: 21 · 2015: 13 · 2014: 20 · 2013: 21 · 2012: 25
- FWCI
- 9.69
- 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
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- content-based recommendation, collaborative filtering, hybrid recommendation, feature weighting, social network analysis, linear regression, item similarity, user preferences
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