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Neural Re-ranking for Multi-stage Recommender Systems

Authors:

Weiwen Liu,

Jiarui Qin,

Ruiming Tang,

Bo ChenAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 698 - 699

https://doi.org/10.1145/3523227.3547369

Published: 13 September 2022 Publication History

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Abstract

Re-ranking is one of the most critical stages for multi-stage recommender systems (MRS), which re-orders the input ranking lists by modeling the cross-item interaction. Recent re-ranking methods have evolved into deep neural architectures due to the significant advances in deep learning. Neural re-ranking, therefore, has become a trending topic and many of the improved algorithms have demonstrated their use in industrial applications, enjoying great commercial success. The purpose of this tutorial is to explore some of the recent work on neural re-ranking, integrating them into a broader picture and paving ways for more comprehensive solutions for future research. In particular, we provide a taxonomy of current methods according to the objectives and training signals. We examine and compare these methods qualitatively and quantitatively, and identify some open challenges and future prospects.

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Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, and Yong Yu. 2022. Multi-Level Interaction Reranking with User Behavior History. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

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Liu WGuo WLiu YTang RWang H(2023)User Behavior Modeling with Deep Learning for Recommendation: Recent AdvancesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609496(1286-1287)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609496

Index Terms

Neural Re-ranking for Multi-stage Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Liu WGuo WLiu YTang RWang H(2023)User Behavior Modeling with Deep Learning for Recommendation: Recent AdvancesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609496(1286-1287)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3609496

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Abstract

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Recommendations

Re-ranking search results using query logs

Listwise Neural Ranking Models

A General Model for Mutual Ranking Systems

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