A modular adversarial approach to social recommendation

Cheruvu, Haricharan

Permalink

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

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

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

Owning Collections