Recommender system based on scarce information mining

Wei Lu, Fu Lai Korris Chung, Kunfeng Lai, Liang Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Nowadays, the highly popular user generated content sites provide various sources of information such as tags for recommendation tasks. Motivated by a real world online video recommendation problem, this work targets at the long tail phenomena of user behavior and the sparsity of item features. A personalized compound recommendation framework for online video recommendation called Dirichlet mixture probit model for information scarcity (DPIS) is hence proposed. Assuming that each clicking sample is generated from a representation of user preferences, DPIS models the sample level topic proportions as a multinomial item vector, and utilizes topical clustering on the user part for recommendation through a probit classifier. As demonstrated by the real-world application, the proposed DPIS achieves better performance in accuracy, perplexity as well as diversity in coverage than traditional methods.
Original languageEnglish
Pages (from-to)256-266
Number of pages11
JournalNeural Networks
Volume93
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • Content-based filtering
  • Latent structure interpretation
  • Probabilistic topic model
  • Recommender system

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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