research-article

Exploiting Intent Evolution in E-commercial Query Recommendation

Authors:

Zhengyang Wang,

Philip S. YuAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5162 - 5173

https://doi.org/10.1145/3580305.3599821

Published: 04 August 2023 Publication History

Abstract

Aiming at a better understanding of the search goals in the user search sessions, recent query recommender systems explicitly model the reformulations of queries, which hopes to estimate the intents behind these reformulations and thus benefit the next-query recommendation. However, in real-world e-commercial search scenarios, user intents are much more complicated and may evolve dynamically. Existing methods merely consider trivial reformulation intents from semantic aspects and fail to model dynamic reformulation intent flows in search sessions, leading to sub-optimal capacities to recommend desired queries. To deal with these limitations, we first explicitly define six types of query reformulation intents according to the desired products of two consecutive queries. We then apply two self-attentive encoders on top of two pre-trained large language models to learn the transition dynamics from semantic query and intent reformulation sequences, respectively. We develop an intent-aware query decoder to utilize the predicted intents for suggesting the next queries. We instantiate such a framework with an Intent-aware Variational AutoEncoder (IVAE) under deployment at Amazon. We conduct comprehensive experiments on two real-world e-commercial datasets from Amazon and one public dataset from BestBuy. Specifically, IVAE improves the Recall@15 by 25.44% and 60.47% on two Amazon datasets and 13.91% on BestBuy, respectively.

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Cited By

Liu YWang YCao YSun LYu P(2024)Visual Summary Thought of Large Vision-Language Models for Multimodal Recommendation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825030(456-461)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825030

Index Terms

Exploiting Intent Evolution in E-commercial Query Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

Copyright © 2023 ACM.

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Published: 04 August 2023

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KDD '23

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Liu YWang YCao YSun LYu P(2024)Visual Summary Thought of Large Vision-Language Models for Multimodal Recommendation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825030(456-461)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825030

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