2026-09-23T02:48:35Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/108180 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Sundaram, Hari Cheruvu, Haricharan 2020-08-26T23:58:45Z 2020-08-26T23:58:45Z 2022-08-26T23:58:55Z 2020-05-12 2020-05 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. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01 The student, Haricharan Cheruvu, accepted the attached license on 2020-05-11 at 12:09. The student, Haricharan Cheruvu, submitted this Thesis for approval on 2020-05-11 at 12:13. This Thesis was approved for publication on 2020-05-12 at 12:00. DSpace SAF Submission Ingestion Package generated from Vireo submission #15319 on 2020-08-25 at 17:30:50 Made available in DSpace on 2020-08-26T23:58:45Z (GMT). No. of bitstreams: 2 CHERUVU-THESIS-2020.pdf: 5605680 bytes, checksum: e684651c3d14a712d0483466650f02f4 (MD5) LICENSE.txt: 4209 bytes, checksum: d2d057ba9e16452d42ba8b4c2d996f73 (MD5) Previous issue date: 2020-05-12 Embargo set by: Seth Robbins for item 115793 Lift date: 2022-08-26T23:58:55Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only application/pdf http://hdl.handle.net/2142/108180 en Copyright 2020 Haricharan Cheruvu Recommender Systems GAN Social Recommendation A modular adversarial approach to social recommendation text Thesis Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.