Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs

DOI10.1287/ijoc.1100.0385zbMath1243.90224OpenAlexW2100386719MaRDI QIDQ2899108

Zan Huang, Daniel D. Zeng

Publication date: 28 July 2012

Published in: INFORMS Journal on Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/ijoc.1100.0385

zbMATH Keywords

bipartite graphcollaborative filteringrecommendation algorithmrandom graph analysis

Mathematics Subject Classification ID

Programming involving graphs or networks (90C35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Filtering in stochastic control theory (93E11) Random graphs (graph-theoretic aspects) (05C80)

Related Items (3)

Location-Aware Real-Time Recommender Systems for Brick-and-Mortar Retailers ⋮ A survey of active learning in collaborative filtering recommender systems ⋮ Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems

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