Latent factor models with additive and hierarchically-smoothed user preferences

WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2013, Pages 385-394

  Ahmed, Amr ^a   Kanagal, Bhargav ^a   Pandey, Sandeep ^b   Josifovski, Vanja ^a   Pueyo, Lluis Garcia ^a   Yuan, Jeff ^c  

^a GOOGLE INC   (United States)

^b TWITTER INC   (United States)

^c YAHOO RESEARCH   (United States)

Author keywords

inference; latent variable models; recomcollaborative filtering; recomfactor models; recommendation

Indexed keywords

ADDITIVE MODELS; BACKWARD-SMOOTHING; BAYESIAN HIERARCHICAL MODEL; COLD START PROBLEMS; COLLABORATIVE FILTERING SYSTEMS; CONTINUOUS ATTRIBUTE; DATA SPARSITY; DISCRETE ATTRIBUTES; EXPERIMENTAL ANALYSIS; FORWARD-FILTERING; INFERENCE; INFERENCE ALGORITHM; LATENT FACTOR MODELS; LATENT VARIABLE MODELS; NEWS ARTICLES; PREFERENCE MODELS; RANDOM EFFECTS; RANKING ALGORITHM; RECOMMENDATION; USER PREFERENCE MODELS;

ALGORITHMS; COMPUTER SUPPORTED COOPERATIVE WORK; DATA MINING; HIERARCHICAL SYSTEMS; QUALITY OF SERVICE; RECOMMENDER SYSTEMS; WEBSITES;

INFERENCE ENGINES;

EID: 84874261331     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2433396.2433445     Document Type: Conference Paper

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