Bayesian Deep Learning with Trust and Distrust in Recommendation Systems

Dimitrios Rafailidis*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Exploiting the selections of social friends and foes can efficiently face the data scarcity of user preferences and the cold-start problem. In this paper, we present a Social Deep Pairwise Learning model, namely SDPL. According to the Bayesian Pairwise Ranking criterion, we design a loss function with multiple ranking criteria based on the selections of users, and those in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinearity in user preferences and the social information of trust and distrust relationships by designing a deep learning architecture. In each backpropagation step, we perform social negative sampling to meet the multiple ranking criteria of our loss function. Our experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature, demonstrate the effectiveness of the proposed approach, outperforming other state-of-the art methods. In addition, we show that our deep learning strategy plays an important role in capturing the nonlinear associations between user preferences and the social information of trust and distrust relationships, and demonstrate that our social negative sampling strategy is a key factor in SDPL.

Original languageEnglish
Title of host publicationWI '19: IEEE/WIC/ACM International Conference on Web Intelligence
EditorsP Barnaghi, G Gottlob, Y Manolopoulos, T Tzouramanis, A Vakali
Pages18-25
Number of pages8
DOIs
Publication statusPublished - 2019
EventIEEE/WIC/ACM International Conference on Web Intelligence: All in the connected world - Thessaloniki, Greece
Duration: 14 Oct 201917 Oct 2019

Conference

ConferenceIEEE/WIC/ACM International Conference on Web Intelligence
Abbreviated titleWI 2019
CountryGreece
CityThessaloniki
Period14/10/1917/10/19

Keywords

  • Pairwise learning
  • collaborative filtering
  • deep learning
  • social relationships

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