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Open access

Product Query Recommendation for Enriching Suggested Q&As

Authors:

Eilon Sheetrit,

Avihai MejerAuthors Info & Claims

CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval

Pages 352 - 357

https://doi.org/10.1145/3627508.3638314

Published: 10 March 2024 Publication History

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Abstract

To help customers who are still in the exploration phase, Web search engines and e-commerce websites often provide relevant Q&As in widgets, such as ‘People Also Ask’ and ‘Customers Also Ask Alexa’, with additional information. In this work, we propose to enrich this customer experience by rendering related products under each Q&A based on an automated online query recommendation. We define what are the tenets for high-quality query recommendations and explain why this challenge is different from the existing query re-writing, query expansion and keyphrase generation methods. We describe a data collection method which uses customer co-click information on a proprietary website in order to successfully guide our model into generating query recommendations that satisfy all tenets. Offline and online evaluation results demonstrate that our proposed approach generates superior query recommendations and brings much more customer engagement over strong baselines.

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Cited By

Chen MZhang KHe ZJing YWang X(2024)RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor SearchProceedings of the VLDB Endowment10.14778/3681954.368195917:11(2735-2749)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681959

Index Terms

Product Query Recommendation for Enriching Suggested Q&As
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
      1. Query reformulation

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Highlights
- A hybrid recommendation system for Q&A documents is proposed.
- Current knowledge needs are identified by sequential clustering.
- Q&A documents in the list are reranked for rapid coverage of all contents.
- Three recommendation ...
Abstract
Question and answer (Q&A) documents are a new type of knowledge document composed of a question part and an answer part. The questions represent knowledge needs, and the answers contain the knowledge that meets these knowledge needs. An overload ...

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Published In

cover image ACM Other conferences

CHIIR '24: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval

March 2024

481 pages

ISBN:9798400704345

DOI:10.1145/3627508

Editors:
Paul Clough
University of Sheffield Information School
,
Morgan Harvey
University of Sheffield Information School
,
Frank Hopfgartner
Universität Koblenz

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 March 2024

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CHIIR '24

CHIIR '24: 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval

March 10 - 14, 2024

Sheffield, United Kingdom

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Overall Acceptance Rate 55 of 163 submissions, 34%

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Cited By

Chen MZhang KHe ZJing YWang X(2024)RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor SearchProceedings of the VLDB Endowment10.14778/3681954.368195917:11(2735-2749)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3681959

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