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A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling

Published: 04 August 2023 Publication History

Abstract

User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.

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  • (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

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  1. A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling

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    cover image ACM Conferences
    KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2023
    5996 pages
    ISBN:9798400701030
    DOI:10.1145/3580305
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    Published: 04 August 2023

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    Author Tags

    1. knowledge transfer
    2. personalization
    3. recommender system
    4. user-tag profiling

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    • Shanghai Artificial Intelligence Innovation and Development Fund

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    • (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

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