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Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System

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Yong LiAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 4787 - 4793

https://doi.org/10.1145/3583780.3615482

Published: 21 October 2023 Publication History

Abstract

Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormous amount of feedback is the most typical characteristic. Specifically, in short-video recommendation, the easiest-to-collect user feedback is theskipping behavior, which leads to two critical challenges for the recommendation model. First, the skipping behavior reflects implicit user preferences, and thus, it is challenging for interest extraction. Second, this kind of special feedback involves multiple objectives, such as total watching time and skipping rate, which is also very challenging. In this paper, we present our industrial solution in Kuaishou1, which serves billion-level users every day. Specifically, we deploy a feedback-aware encoding module that extracts user preferences, taking the impact of context into consideration. We further design a multi-objective prediction module which well distinguishes the relation and differences among different model objectives in the short-video recommendation. We conduct extensive online A/B tests, along with detailed and careful analysis, which verify the effectiveness of our solution.

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Cited By

Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
Chen WBei YShen QChen HHuang XHuang FSerra ESpezzano F(2024)Feedback Reciprocal Graph Collaborative FilteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680015(4397-4405)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680015
Liu JZhang SYan SZhang XRen W(2024)Formal Analysis on Interaction Flow and Information Cocoon Based on Probabilistic Graph2024 IEEE 11th International Conference on Cyber Security and Cloud Computing (CSCloud)10.1109/CSCloud62866.2024.00024(96-100)Online publication date: 28-Jun-2024
https://doi.org/10.1109/CSCloud62866.2024.00024
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Index Terms

Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

General Chairs:
Ingo Frommholz
University of Wolverhampton, UK
,
Frank Hopfgartner
University of Koblenz, Germany
,
Mark Lee
University of Birmingham, UK
,
Michael Oakes
University of Birmingham, UK
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Min Zhang
Tsinghua University, China
,
Rodrygo Santos
Federal University of Minas Gerais, Brazil

Copyright © 2023 ACM.

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Published: 21 October 2023

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the National Natural Science Foundation of China
the National Key Research and Development Program of China

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CIKM '23

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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management

October 21 - 25, 2023

Birmingham, United Kingdom

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Bai YZhang YFeng FLu JZang XLei CSong YBaeza-Yates RBonchi F(2024)GradCraft: Elevating Multi-task Recommendations through Holistic Gradient CraftingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671585(4774-4783)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671585
Chen WBei YShen QChen HHuang XHuang FSerra ESpezzano F(2024)Feedback Reciprocal Graph Collaborative FilteringProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680015(4397-4405)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680015
Liu JZhang SYan SZhang XRen W(2024)Formal Analysis on Interaction Flow and Information Cocoon Based on Probabilistic Graph2024 IEEE 11th International Conference on Cyber Security and Cloud Computing (CSCloud)10.1109/CSCloud62866.2024.00024(96-100)Online publication date: 28-Jun-2024
https://doi.org/10.1109/CSCloud62866.2024.00024
Gu PHu HXu G(2024)Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendationKnowledge-Based Systems10.1016/j.knosys.2024.111841295(111841)Online publication date: Jul-2024
https://doi.org/10.1016/j.knosys.2024.111841
Zhang WTian BWang TYuan LJiang M(2024)Research on Micro-videos Recommendation Method Integrating Multimodal Data and User Multi-behaviorWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_1(3-16)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_1

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