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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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“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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