short-paper

Improving Conversational Recommender Systems via Transformer-based Sequential Modelling

Authors:

Evangelos Kanoulas,

Cheng LongAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2319 - 2324

https://doi.org/10.1145/3477495.3531852

Published: 07 July 2022 Publication History

Abstract

In Conversational Recommender Systems (CRSs), conversations usually involve a set of related items and entities e.g., attributes of items. These items and entities are mentioned in order following the development of a dialogue. In other words, potential sequential dependencies exist in conversations. However, most of the existing CRSs neglect these potential sequential dependencies. In this paper, we propose a Transformer-based sequential conversational recommendation method, named TSCR, which models the sequential dependencies in the conversations to improve CRS. We represent conversations by items and entities, and construct user sequences to discover user preferences by considering both mentioned items and entities. Based on the constructed sequences, we deploy a Cloze task to predict the recommended items along a sequence. Experimental results demonstrate that our TSCR model significantly outperforms state-of-the-art baselines.

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Cited By

Liu YPei JZhang WLi MChe Wde Rijke M(2025)Augmentation with Neighboring Information for Conversational RecommendationACM Transactions on Information Systems10.1145/371258843:3(1-49)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3712588
He ZXie ZSteck HLiang DJha RKallus NMcAuley JNejdl WAuer SKarras OCha MMoens MNajork M(2025)Reindex-Then-Adapt: Improving Large Language Models for Conversational RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703573(866-875)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703573
Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_22
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Index Terms

Improving Conversational Recommender Systems via Transformer-based Sequential Modelling
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
      2. Recommender systems

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Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Liu YPei JZhang WLi MChe Wde Rijke M(2025)Augmentation with Neighboring Information for Conversational RecommendationACM Transactions on Information Systems10.1145/371258843:3(1-49)Online publication date: 17-Jan-2025
https://dl.acm.org/doi/10.1145/3712588
He ZXie ZSteck HLiang DJha RKallus NMcAuley JNejdl WAuer SKarras OCha MMoens MNajork M(2025)Reindex-Then-Adapt: Improving Large Language Models for Conversational RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703573(866-875)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703573
Liu YQi JYu Y(2025)A Review on Deep Learning for Sequential Recommender Systems: Key Technologies and DirectionsBig Data10.1007/978-981-96-1024-2_22(305-318)Online publication date: 24-Jan-2025
https://doi.org/10.1007/978-981-96-1024-2_22
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
Liu QFeng XGu TLiu X(2024)FairCRS: Towards User-oriented Fairness in Conversational Recommendation SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688150(126-136)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688150
Zhang SWang MZhao XGuo RZhao YZhuang CGu JZhang ZYin H(2024)DNS-Rec: Data-aware Neural Architecture Search for Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688117(591-600)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688117
Petruzzelli AMartina ASpillo GMusto CDe Gemmis MLops PSemeraro G(2024)Improving Transformer-based Sequential Conversational Recommendations through Knowledge Graph EmbeddingsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659565(172-182)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659565
Dao HDeng YLe DLiao LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Broadening the View: Demonstration-augmented Prompt Learning for Conversational RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657755(785-795)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657755
Kim SLim JJang J(2024)Patient-Adaptive Beat-Wise Temporal Transformer for Atrial Fibrillation Classification in Continuous Long-Term Cardiac MonitoringIEEE Access10.1109/ACCESS.2024.349804312(172358-172367)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3498043
Ju WZhao YQin YYi SYuan JXiao ZLuo XYan XZhang M(2024)COOLInformation Fusion10.1016/j.inffus.2024.102341107:COnline publication date: 2-Jul-2024
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