Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

Yuan, J., Sivrikaya, F., Hopfgartner, F., Lommatzsch, A. and Mu, M. (2015) Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders. In: 2nd Workshop on Recommendation Systems for Television and Online Video, Vienna, Austria, 19 Sep 2015,

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In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Hopfgartner, Dr Frank
Authors: Yuan, J., Sivrikaya, F., Hopfgartner, F., Lommatzsch, A., and Mu, M.
College/School:College of Arts > School of Humanities > Information Studies
Copyright Holders:Copyright © 2015 The Authors
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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