Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...

With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label,...

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Bibliographic Details
Main Authors: Zhang, Yang, Bai, Yimeng, Chang, Jianxin, Zang, Xiaoxue, Lu, Song, Lu, Jing, Feng, Fuli, Niu, Yanan, Song, Yang
Format: Text
Language:unknown
Published: arXiv 2023
Subjects:
Information Retrieval cs.IR
FOS Computer and information sciences
H.3.3; H.3.5
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2306.17426
https://arxiv.org/abs/2306.17426
id ftdatacite:10.48550/arxiv.2306.17426
record_format openpolar
spelling ftdatacite:10.48550/arxiv.2306.17426 2023-10-01T03:55:39+02:00 Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ... Zhang, Yang Bai, Yimeng Chang, Jianxin Zang, Xiaoxue Lu, Song Lu, Jing Feng, Fuli Niu, Yanan Song, Yang 2023 https://dx.doi.org/10.48550/arxiv.2306.17426 https://arxiv.org/abs/2306.17426 unknown arXiv https://dx.doi.org/10.1145/3583780.3615483 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Information Retrieval cs.IR FOS Computer and information sciences H.3.3; H.3.5 ScholarlyArticle Article article-journal Text 2023 ftdatacite https://doi.org/10.48550/arxiv.2306.1742610.1145/3583780.3615483 2023-09-04T15:17:58Z With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiased Multiple-semantics-extracting Labeling(DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby directly mitigating bias at the label level. We ... : 8 pages, 4 figures ... Text DML DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
topic Information Retrieval cs.IR
FOS Computer and information sciences
H.3.3; H.3.5
spellingShingle Information Retrieval cs.IR
FOS Computer and information sciences
H.3.3; H.3.5
Zhang, Yang
Bai, Yimeng
Chang, Jianxin
Zang, Xiaoxue
Lu, Song
Lu, Jing
Feng, Fuli
Niu, Yanan
Song, Yang
Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
topic_facet Information Retrieval cs.IR
FOS Computer and information sciences
H.3.3; H.3.5
description With the proliferation of short video applications, the significance of short video recommendations has vastly increased. Unlike other recommendation scenarios, short video recommendation systems heavily rely on feedback from watch time. Existing approaches simply treat watch time as a direct label, failing to effectively harness its extensive semantics and introduce bias, thereby limiting the potential for modeling user interests based on watch time. To overcome this challenge, we propose a framework named Debiased Multiple-semantics-extracting Labeling(DML). DML constructs labels that encompass various semantics by utilizing quantiles derived from the distribution of watch time, prioritizing relative order rather than absolute label values. This approach facilitates easier model learning while aligning with the ranking objective of recommendations. Furthermore, we introduce a method inspired by causal adjustment to refine label definitions, thereby directly mitigating bias at the label level. We ... : 8 pages, 4 figures ...
format Text
author Zhang, Yang
Bai, Yimeng
Chang, Jianxin
Zang, Xiaoxue
Lu, Song
Lu, Jing
Feng, Fuli
Niu, Yanan
Song, Yang
author_facet Zhang, Yang
Bai, Yimeng
Chang, Jianxin
Zang, Xiaoxue
Lu, Song
Lu, Jing
Feng, Fuli
Niu, Yanan
Song, Yang
author_sort Zhang, Yang
title Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
title_short Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
title_full Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
title_fullStr Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
title_full_unstemmed Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework ...
title_sort leveraging watch-time feedback for short-video recommendations: a causal labeling framework ...
publisher arXiv
publishDate 2023
url https://dx.doi.org/10.48550/arxiv.2306.17426
https://arxiv.org/abs/2306.17426
genre DML
genre_facet DML
op_relation https://dx.doi.org/10.1145/3583780.3615483
op_rights arXiv.org perpetual, non-exclusive license
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
op_doi https://doi.org/10.48550/arxiv.2306.1742610.1145/3583780.3615483
_version_ 1778524267610112000

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