Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation

Xiaokun Zhang, Bo Xu, Liang Yang, Chenliang Li, Fenglong Ma, Haifeng Liu, Hongfei Lin

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

31 Scopus citations

Abstract

Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.

Original languageEnglish (US)
Title of host publicationSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages1684-1693
Number of pages10
ISBN (Electronic)9781450387323
DOIs
StatePublished - Jul 6 2022
Event45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022 - Madrid, Spain
Duration: Jul 11 2022Jul 15 2022

Publication series

NameSIGIR 2022 - Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference45th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022
Country/TerritorySpain
CityMadrid
Period7/11/227/15/22

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems
  • Software

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