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Multi-view improved sequence behavior with adaptive multi-task learning in ranking

Multi-view improved sequence behavior with adaptive multi-task learning in ranking Click through rate (CTR) and Conversion Rate (CVR) are core tasks in e-commerce recommender systems. Sequence behavior and multi-task learning have been widely used in CTR and CVR. Based on the concept of a transformer, we develop a technique of time and space feature representation for the prediction, which can capture high-level information better. In order to formulate user’s different interests from historical sequence behavior, we design multi-task learning to improve multiple objectives simultaneously. It is difficult to turn the super parameters as the tasks increasing. In this paper, we propose an adaptive learning mixture-of-experts approach, which tackles this challenge and can learn super parameters among tasks automatically. It not only saves resources but also improves the performance with cognitive of the model. Furthermore, to enhance the flexibility, we improve the loss function with a constrained joint strategy and introduce RESNET mechanism. We design feature-cross-unit module, augment-expert module, and topK-dispatch module, which assist multi-task learning to improve better. Experiments on public dataset and our library dataset demonstrate the superiority of our model over the state-of-art method. Our method achieves + 2.29% AUC gain in the CTR task and + 1.81% AUC gain in the CVR task, which is a significant improvement and demonstrates the effectiveness of proposed approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Multi-view improved sequence behavior with adaptive multi-task learning in ranking

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References (41)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04088-w
Publisher site
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Abstract

Click through rate (CTR) and Conversion Rate (CVR) are core tasks in e-commerce recommender systems. Sequence behavior and multi-task learning have been widely used in CTR and CVR. Based on the concept of a transformer, we develop a technique of time and space feature representation for the prediction, which can capture high-level information better. In order to formulate user’s different interests from historical sequence behavior, we design multi-task learning to improve multiple objectives simultaneously. It is difficult to turn the super parameters as the tasks increasing. In this paper, we propose an adaptive learning mixture-of-experts approach, which tackles this challenge and can learn super parameters among tasks automatically. It not only saves resources but also improves the performance with cognitive of the model. Furthermore, to enhance the flexibility, we improve the loss function with a constrained joint strategy and introduce RESNET mechanism. We design feature-cross-unit module, augment-expert module, and topK-dispatch module, which assist multi-task learning to improve better. Experiments on public dataset and our library dataset demonstrate the superiority of our model over the state-of-art method. Our method achieves + 2.29% AUC gain in the CTR task and + 1.81% AUC gain in the CVR task, which is a significant improvement and demonstrates the effectiveness of proposed approach.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Adaptive multi-task; Joint learning; Loss constraint; Resnet multi-layer; Sequence behavior; Feature cross unit; Augment expert; Top-k dispatch

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