An Interpretable Neural Network Model for Bundle Recommendations Doctoral Symposium, Extended Abstract

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

Abstract

A users' preference for a bundle-a set of items that can be purchased together-can be expressed by the utility of this bundle to the user. The multi-attribute utility theory motivate us to characterize the utility of a bundle using its attributes to improve the personalized bundle recommendation systems. This extended abstract for the Doctoral Symposium describes my PhD project for studying the utility of a bundle using its attributes. The steps taken and some preliminary results are presented, with an outline of the future plans.

Original languageEnglish (US)
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages722-723
Number of pages2
ISBN (Electronic)9781450392785
DOIs
StatePublished - Sep 12 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: Sep 18 2022Sep 23 2022

Publication series

NameRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems

Conference

Conference16th ACM Conference on Recommender Systems, RecSys 2022
Country/TerritoryUnited States
CitySeattle
Period9/18/229/23/22

Keywords

  • Bundle recommendation
  • Bundle utility
  • Recommender systems

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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