PARS : Peers-aware Recommender System

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

View graph of relations

Author(s)

  • Huiqiang Mao
  • Chenliang Li
  • Di Chen
  • Xiaoqing Wang
  • Yuming Deng

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationThe Web Conference 2020
Subtitle of host publicationProceedings of The World Wide Web Conference WWW 2020
PublisherAssociation for Computing Machinery, Inc
Pages2606-2612
ISBN (Print)978-1-4503-7023-3
Publication statusPublished - Apr 2020

Publication series

NameThe Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020

Conference

Title29th International World Wide Web Conference (WWW '20)
LocationOnline
PlaceTaiwan
CityTaipei
Period20 - 24 April 2020

Link(s)

Abstract

The presence or absence of one item in a recommendation list will affect the demand for other items because customers are often willing to switch to other items if their most preferred items are not available. The cross-item influence, called “peers effect”, has been largely ignored in the literature. In this paper, we develop a peers-aware recommender system, named PARS. We apply a ranking-based choice model to capture the cross-item influence and solve the resultant MaxMin problem with a decomposition algorithm. The MaxMin model solves for the recommendation decision in the meanwhile of estimating users’ preferences towards the items, which yields high-quality recommendations robust to input data variation. Experimental results illustrate that PARS outperforms a few frequently used methods in practice. An online evaluation with a flash sales scenario at Taobao also shows that PARS delivers significant improvements in terms of both conversion rates and user value.

Research Area(s)

  • E-commerce, Recommender system, Ranking-based model, Demand substitution

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

PARS : Peers-aware Recommender System. / Mao, Huiqiang; Li, Yanzhi; Li, Chenliang; Chen, Di; Wang, Xiaoqing; Deng, Yuming.

The Web Conference 2020: Proceedings of The World Wide Web Conference WWW 2020. Association for Computing Machinery, Inc, 2020. p. 2606-2612 (The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

Download Statistics

No data available