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A Peep into the Future: Adversarial Future Encoding in Recommendation

Published: 15 February 2022 Publication History

Abstract

Personalized recommendation often relies on user historical behaviors to provide items for users. It is intuitive that future information also contains essential messages as supplements to user historical behaviors. However, we cannot directly encode future information into models, since we are unable to get future information in online serving. In this work, we propose a novel adversarial future encoding (AFE) framework to make full use of informative future features in different types of recommendation models. Specifically, AFE contains a future-aware discriminator and a generator. The future-aware discriminator takes both common features and future features as inputs, working as a recommendation prophet to judge user-item pairs. In contrast, the generator is considered as a challenger, which generates items with only common features, aiming to confuse the future-aware prophet. The future-aware discriminator can inspire the generator (to be deployed online) to produce better results. We further conduct a multi-factor optimization to enable a fast and stable model convergence via the direct learning and knowledge distillation losses. Moreover, we have adopted AFE on both a list-wise RL-based ranking model and a point-wise ranking model to verify its universality. In experiments, we conduct sufficient evaluations on two large-scale datasets, achieving significant improvements on both offline and online evaluations. Currently, we have deployed AFE on a real-world system, affecting millions of users. The source code is in https://github.com/modriczhang/AFE.

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    cover image ACM Conferences
    WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
    February 2022
    1690 pages
    ISBN:9781450391320
    DOI:10.1145/3488560
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    Published: 15 February 2022

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    1. future information
    2. gan
    3. recommendation

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    View all
    • (2024)Privileged Knowledge State Distillation for Reinforcement Learning-based Educational Path RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671872(1621-1630)Online publication date: 25-Aug-2024
    • (2024)Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document RankingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679661(2564-2574)Online publication date: 21-Oct-2024
    • (2024)A Survey on Reinforcement Learning for Recommender SystemsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328016135:10(13164-13184)Online publication date: Oct-2024
    • (2023)Workshop on Learning and Evaluating Recommendations with Impressions (LERI)Proceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608756(1248-1251)Online publication date: 14-Sep-2023
    • (2023)Future Augmentation with Self-distillation in RecommendationMachine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track10.1007/978-3-031-43427-3_36(602-618)Online publication date: 18-Sep-2023

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