research-article

Learning to Ask: Conversational Product Search via Representation Learning

Authors:

Evangelos KanoulasAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 2

Article No.: 45, Pages 1 - 27

https://doi.org/10.1145/3555371

Published: 21 December 2022 Publication History

Abstract

Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users’ product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.

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Index Terms

Learning to Ask: Conversational Product Search via Representation Learning
1. Information systems
  1. Information retrieval

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 2

April 2023

770 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3568971

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 21 December 2022

Online AM: 09 August 2022

Accepted: 23 July 2022

Revised: 27 March 2022

Received: 31 August 2021

Published in TOIS Volume 41, Issue 2

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NWO Smart Culture–Big Data/Digital Humanities
NWO Innovational Research Incentives Scheme Vidi
H2020-EU.3.4.–SOCIETAL CHALLENGES–Smart, Green and Integrated Transport
Natural Science Foundation of China
Google Faculty Research Awards program
Natural Sciences and Engineering Research Council (NSERC)
York Research Chairs (YRC)

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Cited By

Huang YHuang J(2024)Exploring ChatGPT for next-generation information retrieval: Opportunities and challengesWeb Intelligence10.3233/WEB-23036322:1(31-44)Online publication date: 26-Mar-2024
https://doi.org/10.3233/WEB-230363
Zou JSun ALong CKanoulas E(2024)Knowledge-Enhanced Conversational Recommendation via Transformer-Based Sequential ModelingACM Transactions on Information Systems10.1145/367737642:6(1-27)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3677376
Ma HZou JAliannejadi MKanoulas EBin YYang YSerra ESpezzano F(2024)Ask or Recommend: An Empirical Study on Conversational Product SearchProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679875(3927-3931)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679875
Landoni MHuibers TMurgia EPera M(2024)Good for Children, Good for All?Advances in Information Retrieval10.1007/978-3-031-56066-8_24(302-313)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56066-8_24

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