Downside management in recommender systems

Huan Gui, Haishan Liu, Xiangrui Meng, Anmol Bhasin, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recommender systems, bad recommendations can lead to a net utility loss for both users and content providers. The downside (individual loss) management is a crucial and important problem, but has long been ignored. We propose a method to identify bad recommendations by modeling the users' latent preferences that are yet to be captured using a residual model, which can be applied independently on top of existing recommendation algorithms. We include two components in the residual utility: benefit and cost, which can be learned simultaneously from users' observed interactions with the recommender system. We further classify user behavior into fine-grained categories, based on which an efficient optimization algorithm to estimate the benefit and cost using Bayesian partial order is proposed. By accurately calculating the utility users obtained from recommendations based on the benefit-cost analysis, we can infer the optimal threshold to determine the downside portion of the recommender system. We validate the proposed method by experimenting with real-world datasets and demonstrate that it can help to prevent bad recommendations from showing.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
EditorsRavi Kumar, James Caverlee, Hanghang Tong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages394-401
Number of pages8
ISBN (Electronic)9781509028467
DOIs
StatePublished - Nov 21 2016
Event2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016 - San Francisco, United States
Duration: Aug 18 2016Aug 21 2016

Publication series

NameProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016

Other

Other2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2016
CountryUnited States
CitySan Francisco
Period8/18/168/21/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Sociology and Political Science
  • Communication

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