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|Title:||Tagcloud-based explanation with feedback for recommender systems|
|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|
|Appears in Collections:||Staff Publications|
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