Zhang, L., Shi, L., Zhao, J. et al. (4 more authors) (2022) A GNN-based multi-task learning framework for personalized video search. In: WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, 21-25 Feb 2022, Virtual Event (Phoenix, AZ, USA). Association for Computing Machinery , pp. 1386-1394. ISBN 9781450391320
Abstract
Watching online videos has become more and more popular and users tend to watch videos based on their personal tastes and preferences. Providing a customized ranking list to maximize the user's satisfaction has become increasingly important for online video platforms. Existing personalized search methods (PSMs) train their models with user feedback information (e.g. clicks). However, we identified that such feedback signals may indicate attractiveness but not necessarily indicate relevance in video search. Besides, the click data and user historical information are usually too sparse to train a good PSM, which is different from the conventional Web search containing users' rich historical information. To address these concerns, in this paper we propose a multi-task graph neural network architecture for personalized video search (MGNN-PVS) that can jointly model user's click behaviour and the relevance between queries and videos. To relieve the sparsity problem and learn better representation for users, queries and videos, we develop an efficient and novel GNN architecture based on neighborhood sampling and hierarchical aggregation strategy by leveraging their different hops of neighbors in the user-query and query-document click graph. Extensive experiments on a major commercial video search engine show that our model significantly outperforms state-of-the-art PSMs, which illustrates the effectiveness of our proposed framework.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Personalized Video Search; Graph Neural Networks; Multi-Task Learning; User-query Graph; Query-document Click Graph |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Dec 2021 10:45 |
Last Modified: | 16 Mar 2022 14:09 |
Status: | Published |
Publisher: | Association for Computing Machinery |
Refereed: | Yes |
Identification Number: | 10.1145/3488560.3498507 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:181816 |