Conceptual collaborative filtering recommendation: A probabilistic learning approach

Jae won Lee, Han Joon Kim, Sang goo Lee

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Collaborative filtering is one of the most successful and popular methods in developing recommendation systems. However, conventional collaborative filtering methods suffer from item sparsity and new item problems. In this paper, we propose a probabilistic learning approach that solves the item sparsity problem while describing users and items with domain concepts. Our method uses a probabilistic match with domain concepts, whereas conventional collaborative filtering uses an exact match to find similar users. Empirical experiments show that our method outperforms the conventional ones.

Original languageEnglish
Pages (from-to)2793-2796
Number of pages4
Issue number13-15
StatePublished - Aug 2010


  • Collaborative filtering
  • Concept
  • Information retrieval
  • Probabilistic learning
  • Recommendation systems


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