Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

9-2016

Abstract

Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek tolearn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.

Keywords

user preferences, homophily, representation learning, social recommendation, restricted Boltzmann machine

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

RecSys '16: Proceedings of the 10th ACM Conference on Recommender Systems: Boston, September 15-19

First Page

317

Last Page

324

ISBN

9781450340359

Identifier

10.1145/2959100.2959157

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2959100.2959157

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