Work

Hybrid Recommender System based on Autoencoders

Year: 2016

Type: conference-paper

Abstract: A standard model for Recommender Systems is the Matrix Completion setting: given partially known matrix of ratings given by users (rows) to items (columns), infer the unknown ratings. In the last decades, few attempts where done to handle that objective with Neural Networks, but recently an architecture based on Autoencoders proved to be a promising approach. In current paper, we enhanced that architecture (i) by using a loss function adapted to input data with missing values, and (ii) by incorporating side information. The experiments demonstrate that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/items.

Source: Workshop on Deep Learning for Recommender Systems (DLRS)

DOI: 10.1145/2988450.2988456

Authors: Florian Strub, Romaric Gaudel, Jérémie Mary

Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Université de Lille, Centrale Lille Institut

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, Centre National de la Recherche Scientifique, CHIST-ERA

OpenAlex ID: W2471920251

FWCI: 18.9

Cites: 56

Cited by: 236

Related to: 10