A comparison of collaborative-filtering algorithms for ecommerce

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Abstract

Various Collaborative Filtering (CF) recommendation algorithms characterize consumers and products by the data available about consumer-product interactions in e-commerce applications. The user-based algorithm predicts a target consumer's future transactions by aggregating the observed transactions of similar consumers. The item-based algorithm computes product similarities instead of consumer similarities and gives the products' potential scores for reach consumer. The generative-model algorithm uses latent class variables to explain the patterns of interactions between consumers and products. The spreading-activation algorithm addresses the sparsity problem by exploring transitive associations between consumers and products in a bipartite consumer-product graph. The link-analysis algorithms adapts Hypertext-Induced Topic Selection (HITS) algorithm in the recommendation context.

Original languageEnglish (US)
Pages (from-to)68-78
Number of pages11
JournalIEEE Intelligent Systems
Volume22
Issue number5
DOIs
StatePublished - Sep 1 2007

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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