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Title: Pre-training graph transformer with multimodal side information for recommendation
Authors: Liu, Yong
Yang, Susen
Lei, Chenyi
Wang, Guoxin
Tang, Haihong
Zhang, Juyong
Sun, Aixin
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Liu, Y., Yang, S., Lei, C., Wang, G., Tang, H., Zhang, J., Sun, A. & Miao, C. (2021). Pre-training graph transformer with multimodal side information for recommendation. 29th ACM International Conference on Multimedia (MM '21), 2853-2861.
Project: AISG-GC2019-003 
metadata.dc.contributor.conference: 29th ACM International Conference on Multimedia (MM '21)
Abstract: Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate items by common user activities, e.g., co-purchase, and construct a homogeneous item graph. This graph provides a unified view of item relations and their associated side information in multimodality. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction. Experimental results on real datasets demonstrate that the proposed PMGT model effectively exploits the multimodality side information to achieve better accuracies in downstream tasks including item recommendation and click-through ratio prediction. In addition, we also report a case study of testing PMGT in an online setting with 600 thousand users.
ISBN: 9781450386517
DOI: 10.1145/3474085.3475709
Schools: School of Computer Science and Engineering 
Research Centres: Alibaba-NTU Singapore Joint Research Institute & LILY Research Centre
Rights: © 2021 Association for Computing Machinery. All rights reserved. This paper was published in Proceedings of the 29th ACM International Conference on Multimedia (MM '21) and is made available with permission of Association for Computing Machinery.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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