Please use this identifier to cite or link to this item: https://doi.org/10.1145/2484028.2484108
Title: Tagcloud-based explanation with feedback for recommender systems
Authors: Chen, W.
Hsu, W. 
Lee, M.L. 
Keywords: Explanation
Personalization
Recommendation
Social tags
Tensor factorization
Issue Date: 2013
Source: Chen, W.,Hsu, W.,Lee, M.L. (2013). Tagcloud-based explanation with feedback for recommender systems. SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval : 945-948. ScholarBank@NUS Repository. https://doi.org/10.1145/2484028.2484108
Abstract: Personalized recommender systems aim to push only the relevant items and information directly to the users without requiring them to browse through millions of web resources. The challenge of these systems is to achieve a high user acceptance rate on their recommendations. In this paper, we aim to increase the user acceptance of recommendations by providing more intuitive tag-based explanations of why the items are recommended. Tags are used as intermediary entities that not only relate target users to the recommended items but also understand users' intents. Our system also allows tag-based online relevance feedback. Experiment results on the Movielens dataset show that the proposed approach is able to increase the acceptance rate of recommendations and improve user satisfaction. Copyright © 2013 ACM.
Source Title: SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/78370
ISBN: 9781450320344
DOI: 10.1145/2484028.2484108
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