research-article

Personalized, Sequential, Attentive, Metric-Aware Product Search

Authors:

Shangsong Liang,

Qiang ZhangAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2

Article No.: 36, Pages 1 - 29

https://doi.org/10.1145/3473337

Published: 24 November 2021 Publication History

Abstract

The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

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Index Terms

Personalized, Sequential, Attentive, Metric-Aware Product Search
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 40, Issue 2

April 2022

587 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3484931

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Publication History

Published: 24 November 2021

Accepted: 01 June 2021

Revised: 01 May 2021

Received: 01 August 2020

Published in TOIS Volume 40, Issue 2

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Cited By

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https://dl.acm.org/doi/10.1145/3663760
Wang YJavari ABalaji JShalaby WDerr TCui XChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Knowledge Graph-based Session Recommendation with Session-Adaptive PropagationCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648324(264-273)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648324
Hsu CChen CHuang S(2023)Adaptive Adversarial Contrastive Learning for Cross-Domain RecommendationACM Transactions on Knowledge Discovery from Data10.1145/363025918:3(1-34)Online publication date: 9-Dec-2023
https://dl.acm.org/doi/10.1145/3630259
Jing MZhu YZang TWang K(2023)Contrastive Self-supervised Learning in Recommender Systems: A SurveyACM Transactions on Information Systems10.1145/362715842:2(1-39)Online publication date: 8-Nov-2023
https://dl.acm.org/doi/10.1145/3627158
Wan ZLiu XWang BQiu JLi BGuo TChen GWang Y(2023)Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based RecommendationACM Transactions on Information Systems10.1145/362609142:2(1-26)Online publication date: 8-Nov-2023
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Yang YZhang JWang YMiao ZTong Y(2023)Multiple Connectivity Views for Session-based RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608861(1000-1006)Online publication date: 14-Sep-2023
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Jia BCao JQian SZhu NDong XZhang LCheng LMo L(2023)SMONE: A Session-based Recommendation Model Based on Neighbor Sessions with Similar Probabilistic IntentionsACM Transactions on Knowledge Discovery from Data10.1145/358709917:8(1-22)Online publication date: 12-May-2023
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