research-article

Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services

Authors:

Yue QiAuthors Info & Claims

WWW '22: Companion Proceedings of the Web Conference 2022

Pages 1167 - 1175

https://doi.org/10.1145/3487553.3524729

Published: 16 August 2022 Publication History

Abstract

Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly suggest exposed items while ignoring less-active ones. Additional problems exist in multi-scenario platforms, e.g. appropriate data fusion from subsidiary scenarios, which we observe could be alleviated through graph structured data integration via message passing.

In this paper, we present a multi-graph structured multi-scenario recommendation solution, which encapsulates interaction data across scenarios with multi-graph and obtains representation via graph learning. Extensive offline and online experiments on real-world datasets are conducted where the proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video Views per capita on new users over deployed set of baselines and outperforms regular method in increasing the number of outer-scenario videos by 25% and video watches by 116%, validating its superiority in activating cold videos and enriching target recommendation.

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Cited By

Deepak GIyer KShukla S(2024)ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)10.1109/ICWITE59797.2024.10503160(594-599)Online publication date: 16-Feb-2024
https://doi.org/10.1109/ICWITE59797.2024.10503160
Ning WYan XLiu WCheng RZhang RTang BFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Multi-domain Recommendation with Embedding Disentangling and Domain AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614977(1917-1927)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614977
Wang YZhao XChen BLiu QGuo HLiu HWang YZhang RTang RChen HDuh WHuang HKato MMothe JPoblete B(2023)PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591750(1498-1507)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591750
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Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services

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Published In

cover image ACM Conferences

WWW '22: Companion Proceedings of the Web Conference 2022

April 2022

1338 pages

ISBN:9781450391306

DOI:10.1145/3487553

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Lionel Médini
Université Lyon 1, France
,
Ivan Herman
W3C / retired

Copyright © 2022 ACM.

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Published: 16 August 2022

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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

Deepak GIyer KShukla S(2024)ATRSI: Automatic Tag Recommendation for Videos Encompassing Semantic Intelligence2024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)10.1109/ICWITE59797.2024.10503160(594-599)Online publication date: 16-Feb-2024
https://doi.org/10.1109/ICWITE59797.2024.10503160
Ning WYan XLiu WCheng RZhang RTang BFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Multi-domain Recommendation with Embedding Disentangling and Domain AlignmentProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614977(1917-1927)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614977
Wang YZhao XChen BLiu QGuo HLiu HWang YZhang RTang RChen HDuh WHuang HKato MMothe JPoblete B(2023)PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario RecommendationsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591750(1498-1507)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591750
Celikik MWasilewski JMbarek SCelayes PGagliardi PPham DKaressli NRamallo A(2023)Reusable Self-attention-Based Recommender System for FashionRecommender Systems in Fashion and Retail10.1007/978-3-031-22192-7_3(45-61)Online publication date: 2-Mar-2023
https://doi.org/10.1007/978-3-031-22192-7_3

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