Scarce feature topic mining for video recommendation

Wei Lu, Fu Lai Korris Chung, Kunfeng Lai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Recommendation for user generated content sites has gained significant attention. To satisfy the niche tastes of users, product recommendation poses more challenges due to the data sparsity issue. This work is motivated by a real world online video recommendation problem, where the click records database suffers from s-parseness of video inventory and video tags. Targeting the long tail phenomena of user behavior and sparsity of item features, we propose a personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS). Assuming that each record is generated from a representation of user preferences, DPIS is a probit classifier utilizing record topical clustering on the user part for recommendation. As demonstrated by the real-world application, the proposed DPIS achieves better performance than traditional methods.
Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450340731
Publication statusPublished - 24 Oct 2016
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016


Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States


  • Bayesian approach
  • Recommender system
  • Topic model

ASJC Scopus subject areas

  • General Business,Management and Accounting
  • General Decision Sciences


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