Gaussian Embeddings for Collaborative Filtering

Ludovic Dos Santos 1 Benjamin Piwowarski 2 Patrick Gallinari 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
2 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Most collaborative ltering systems, such as matrix factorization, use vector representations for items and users. Those representations are deterministic, and do not allow modeling the uncertainty of the learned representation, which can be useful when a user has a small number of rated items (cold start), or when there is connict-ing information about the behavior of a user or the ratings of an item. In this paper, we leverage recent works in learning Gaussian embeddings for the recommendation task. We show that this model performs well on three representative collections (Yahoo, Yelp and MovieLens) and analyze learned representations. CCS CONCEPTS • Information systems → Recommender systems; KEYWORDS recommender system, gaussian embeddings, probabilistic representation ACM Reference format:
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40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug 2017, Tokyo, Japan. ACM, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1065-1068, 2017, 〈http://sigir.org/sigir2017〉. 〈10.1145/3077136.3080722〉
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Contributeur : Benjamin Piwowarski <>
Soumis le : mercredi 6 septembre 2017 - 09:06:30
Dernière modification le : vendredi 31 août 2018 - 09:25:57

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Ludovic Dos Santos, Benjamin Piwowarski, Patrick Gallinari. Gaussian Embeddings for Collaborative Filtering. 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Aug 2017, Tokyo, Japan. ACM, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp.1065-1068, 2017, 〈http://sigir.org/sigir2017〉. 〈10.1145/3077136.3080722〉. 〈hal-01582488〉

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