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

Jing Yuan, Fikret Sivrikaya, Frank Hopfgartner, Andreas Lommatzsch, Mu Mu

Research output: Contribution to ConferencePaperpeer-review


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.
Original languageEnglish
Number of pages8
Publication statusPublished - 19 Sep 2015
EventThe 9th ACM Conference on Recommender Systems - Vienna, Austria
Duration: 16 Sep 201520 Sep 2015


ConferenceThe 9th ACM Conference on Recommender Systems

Bibliographical note

ISBN: 978-1-4503-3692-5


  • TV recommender
  • context-aware
  • Latent Dirichlet Allocation
  • ranking
  • diversity


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