Personalized item recommendation for second-hand trading platform

Xuzheng Yu, Tian Gan, Yinwei Wei, Zhiyong Cheng, Liqiang Nie

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

7 Citations (Scopus)

Abstract

With rising awareness of environment protection and recycling, second-hand trading platforms have attracted increasing attention in recent years. The interaction data on second-hand trading platforms, consisting of sufficient interactions per user but rare interactions per item, is different from what they are on traditional platforms. Therefore, building successful recommendation systems in the second-hand trading platforms requires balancing modeling items? and users? preference, and mitigating the adverse effects of the sparsity, which makes recommendation especially challenging. Accordingly, we proposed a method to simultaneously learn representations of items and users from coarse-grained and fine-grained features, and a multi-task learning strategy is designed to address the issue of data sparsity. Experiments conducted on a real-world second-hand trading platform dataset demonstrate the effectiveness of our proposed model.

Original languageEnglish
Title of host publicationProceedings of the 28th ACM International Conference on Multimedia
EditorsPradeep K. Atrey, Zhu Li
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages3478-3486
Number of pages9
ISBN (Electronic)9781450379885
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventACM International Conference on Multimedia 2020 - Online, United States of America
Duration: 12 Oct 202016 Oct 2020
Conference number: 28th
https://dl.acm.org/doi/proceedings/10.1145/3394171 (Proceedings)

Conference

ConferenceACM International Conference on Multimedia 2020
Abbreviated titleMM 2020
Country/TerritoryUnited States of America
Period12/10/2016/10/20
Internet address

Keywords

  • recommendation
  • second-hand trading platform
  • sparsity

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