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Search-based Time-aware Graph-enhanced Recommendation with Sequential Behavior Data

Published: 31 July 2024 Publication History

Abstract

Extending from sequential recommendation models, in this article, we present a novel framework named Search-based Time-Aware Recommendation (STARec), which first retrieves the historical behaviors of the given user through a search-based retriever and then captures the user’s evolving demands over time through a time-aware sequential network. We notice that the key insight of STARec is to use the feature and labels to augment the representations, and thus the effectiveness of STARec relies on the acquisition of rich browsing records of the target user and powerful representation of each browsed item and thus its performance could heavily drop regarding long-tail users and items. To this end, we extend STARec by constructing a graph upon the user–item interactions and leveraging the graph structure to enhance the representation learning. We call this extended version Search-based Time-Aware Graph-Enhanced Recommendation (STAGE). We conduct extensive experiments on three real-world datasets and STARec achieves consistent superiority. We further compare STAGE against STARec long-tail users and our results demonstrate that STAGE could outperform STARec at most cases. Results of online A/B tests show that STARec and STAGE achieve an average click-through rate improvement of around 6% and 1.5% in the two main item recommendation scenarios, respectively.

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  • (2025)Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendationThe Journal of Supercomputing10.1007/s11227-025-06925-481:2Online publication date: 21-Jan-2025

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Published In

cover image ACM Transactions on Recommender Systems
ACM Transactions on Recommender Systems  Volume 2, Issue 4
December 2024
210 pages
EISSN:2770-6699
DOI:10.1145/3613743
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 July 2024
Online AM: 27 June 2023
Accepted: 02 June 2023
Revised: 01 June 2023
Received: 29 October 2022
Published in TORS Volume 2, Issue 4

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Author Tags

  1. Recommender system
  2. search-based model
  3. time-aware sequential network
  4. graph representation learning
  5. label trick

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  • Research-article

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  • China Merchants Bank Credit Card Center
  • The Shanghai Jiao Tong University Team
  • Shanghai Municipal Science and Technology Major Project
  • National Natural Science Foundation of China

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  • (2025)Multi-time-scale with clockwork recurrent neural network modeling for sequential recommendationThe Journal of Supercomputing10.1007/s11227-025-06925-481:2Online publication date: 21-Jan-2025

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