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Recommendation from raw data with adaptive compound Poisson factorization

Gouvert, Olivier and Oberlin, Thomas and Févotte, Cédric Recommendation from raw data with adaptive compound Poisson factorization. (2019) In: Conference on Uncertainty in Artificial Intelligence - UAI 2019, 22 July 2019 - 25 July 2019 (Tel Aviv, Israel).

(Document in English)

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Count data are often used in recommender sys-tems: they are widespread (song play counts,product purchases, clicks on web pages) andcan reveal user preference without any explicitrating from the user. Such data are known to besparse, over-dispersed and bursty, which makestheir direct use in recommender systems chal-lenging, often leading to pre-processing stepssuch as binarization. The aim of this paper isto build recommender systems from these rawdata, by means of the recently proposed com-pound Poisson Factorization (cPF). The papercontributions are three-fold: we present a uni-fied framework for discrete data (dcPF), lead-ing to an adaptive and scalable algorithm; weshow that our framework achieves a trade-offbetween Poisson Factorization (PF) applied toraw and binarized data; we study four specificinstances that are relevant to recommendationand exhibit new links with combinatorics. Ex-periments with three different datasets showthat dcPF is able to effectively adjust to over-dispersion, leading to better recommendationscores when compared with PF on either rawor binarized data.

Item Type:Conference or Workshop Item (Paper)
HAL Id:hal-02887543
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
European Research Council - Agence Nationale pour la Recherche (ANR)
Deposited On:01 Jul 2020 09:01

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