University of Illinois Urbana-Champaign

A modular adversarial approach to social recommendation

Cheruvu, Haricharan

Content Files
CHERUVU-THESIS-2020.pdf

Permalink

https://hdl.handle.net/2142/108180

Description

Title
A modular adversarial approach to social recommendation
Author(s)
Cheruvu, Haricharan
Issue Date
2020-05-12
Director of Research (if dissertation) or Advisor (if thesis)
Sundaram, Hari
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-08-26T23:58:45Z
Keyword(s)
  • Recommender Systems
  • GAN
  • Social Recommendation
Abstract
This thesis proposes a novel framework to incorporate social regularization for item recommendation. Social regularization grounded in ideas of homophily and influence appears to capture latent user preferences. However, there are two key challenges: first, the importance of a specific social link depends on the context and second, a fundamental result states that we cannot disentangle homophily and influence from observational data to determine the effect of social inference. Thus we view the attribution problem as inherently adversarial where we examine two competing hypothesis -social influence and latent interests - to explain each purchase decision. We make two contributions. First, we propose a modular, adversarial framework that decouples the architectural choices for the recommender and social representation models, for social regularization. Second, we overcome degenerate solutions through an intuitive contextual weighting strategy, that supports an expressive attribution, to ensure informative social associations play a larger role in regularizing the learned user interest space. Our results indicate significant gains (5-10% relative Recall@K) over state-of-the-art baselines across multiple publicly available datasets.
Graduation Semester
2020-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/108180
Copyright and License Information
Copyright 2020 Haricharan Cheruvu

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