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User-tag Profile Modeling in Recommendation System via Contrast Weighted Tag Masking

Published: 14 August 2022 Publication History

Abstract

User-tag profile modeling has become one of the novel and significant trends for the future development of industrial recommendation systems, which can be divided into two fundamental tasks: User Preferred Tag (UPT) and Tag Preferred User (TPU) in practical scenarios. In most existing deep learning models for user-tag profiling, the network inputs all the combined tags of the item with the user features when training but inputs only one tag with the user feature to evaluate the user's preference on a single tag when testing. This leads to data discrepancy between the training and testing samples. To address such an issue, we attempt a novel Random Masking Model (RMM) to remain only one tag at the training time by masking. However, it causes two other serious downsides. First, not all tags attached to the same item are equally predictive. Irrelevant tags may introduce noisy signals and thus cause performance degradation. Second, it neglects the impact of combined tags aggregated together, which may be an essential factor leading to user clicks. Therefore, we further propose a framework called Contrast Weighted Tag Masking (CWTM) in this work, which tackles these two issues with two modules: (i) Weighted Masking Module (WMM) introduces the importance network to compute a score for each tag attached to the item and then samples from these tags weightedly according to the score; (ii) Contrast Module (CM) makes use of a contrastive learning architecture to inherit and distill some understanding about the effect of aggregated tags. Offline experiments on four datasets (three public datasets and one proprietary industrial dataset) demonstrate the superiority and effectiveness of CWTM over the state-of-the-art baselines. Moreover, CWTM has been deployed on the training platform of Alibaba advertising systems and achieved substantial improvements of ROI and CVR by 16.8% and 9.6%, respectively.

Supplemental Material

MP4 File
The presentation video of the paper "User-tag Profile Modeling in Recommender System via Contrast Weighted Tag Masking". This video introduces the user-tag profiling issues faced by industry and contributions proposed by the paper to address them. In the video, the author illustrates their method in detail and displays the experimental results of the model improvements by their method.

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  • (2024)Multi-Hop Multi-View Memory Transformer for Session-Based RecommendationACM Transactions on Information Systems10.1145/366376042:6(1-28)Online publication date: 11-Jul-2024
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  • (2024)Mapping Item-wise Rating into Attribute-wise Ratings for Enhanced Personalized Recommendations2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724325(1-6)Online publication date: 24-Jun-2024
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    cover image ACM Conferences
    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2022
    5033 pages
    ISBN:9781450393850
    DOI:10.1145/3534678
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    Published: 14 August 2022

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    Author Tags

    1. contrastive learning
    2. personalization
    3. recommender system
    4. user-tag profile

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    • New Generation of AI 2030
    • Alibaba Innovation Research
    • Shanghai Municipal Science and Technology Major Project

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    Cited By

    View all
    • (2024)Multi-Hop Multi-View Memory Transformer for Session-Based RecommendationACM Transactions on Information Systems10.1145/366376042:6(1-28)Online publication date: 11-Jul-2024
    • (2024)Modeling Domains as Distributions with Uncertainty for Cross-Domain RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657930(2517-2521)Online publication date: 10-Jul-2024
    • (2024)Mapping Item-wise Rating into Attribute-wise Ratings for Enhanced Personalized Recommendations2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT61001.2024.10724325(1-6)Online publication date: 24-Jun-2024
    • (2023)Interpretable User Retention Modeling in RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608818(702-708)Online publication date: 14-Sep-2023
    • (2023)A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile ModelingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599761(5739-5749)Online publication date: 6-Aug-2023

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