GRSAT: A Novel Method on Group Recommendation by Social Affinity and Trustworthiness
- Authors
- Hong, Minsung; Jung, Jason J.; Camacho, David
- Issue Date
- Mar-2017
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- Aggregation strategy; consensus function; group recommender system; social affinity; trustworthiness
- Citation
- CYBERNETICS AND SYSTEMS, v.48, no.3, pp 140 - 161
- Pages
- 22
- Journal Title
- CYBERNETICS AND SYSTEMS
- Volume
- 48
- Number
- 3
- Start Page
- 140
- End Page
- 161
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/6193
- DOI
- 10.1080/01969722.2016.1276770
- ISSN
- 0196-9722
1087-6553
- Abstract
- Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based on Social Affinity and Trustworthiness (GRSAT) based on social affinity and trustworthiness, which is obtained from the user's watching-history and content features, without rating-score. Our experiment proves that GRSAT has outstanding performance for group recommendation compared with the other consensus functions, in terms of the number of the movies and users, on both biased and unbiased groups.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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