DMFP: A Dynamic Multi-faceted Fine-Grained Preference Model for Recommendation

Wang, Huizhao; Liu, Guanfeng; Zhao, Yan; Zheng, Bolong; Zheng, Kai

doi:10.1109/icdm.2019.00071

Cited by 7 publications

(1 citation statement)

References 21 publications

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“…They can be regarded as complement work with our approach. State-of-the-art methods have found the effectiveness of modeling users' historical behaviors for CTR prediction [8,10,16,17,26,27,31,40,41]. DIN [41] notices that a user may have multiple interests and uses attention mechanism to learn the representation of user interests from historical behaviors with respect to a certain candidate item.…”

Section: Related Work 21 Ctr Predictionmentioning

confidence: 99%

Deep Multifaceted Transformers for Multi-objective Ranking in Large-Scale E-commerce Recommender Systems

Ding

Wang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Recommender Systems have been playing essential roles in ecommerce portals. Existing recommendation algorithms usually learn the ranking scores of items by optimizing a single task (e.g., Click-through rate prediction) based on users' historical click sequences, but they generally pay few attention to simultaneously modeling users' multiple types of behaviors or jointly optimize multiple objectives (e.g., both Click-through rate and Conversion rate), which are both vital for e-commerce sites. In this paper, we argue that it is crucial to formulate users' different interests based on multiple types of behaviors and perform multi-task learning for significant improvement in multiple objectives simultaneously. We propose Deep Multifaceted Transformers (DMT), a novel framework that can model users' multiple types of behavior sequences simultaneously with multiple Transformers. It utilizes Multi-gate Mixture-of-Experts to optimize multiple objectives. Besides, it exploits unbiased learning to reduce the selection bias in the training data. Experiments on JD real production dataset demonstrate the effectiveness of DMT, which significantly outperforms state-ofart methods. DMT has been successfully deployed to serve the main traffic in the commercial Recommender System in JD.com. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/CIKM2020_DMT.

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