FEDIN improves CTR prediction by using target-aware frequency filtering to isolate low-entropy periodic interest signals from high-entropy noise in user attention patterns.
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Deep learning based methods have been widely used in industrial recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm: raw features are embedded into low-dimensional vectors, which are then fed on to MLP for final recommendations. However, most of these works just concatenate different features, ignoring the sequential nature of users' behaviors. In this paper, we propose to use the powerful Transformer model to capture the sequential signals underlying users' behavior sequences for recommendation in Alibaba. Experimental results demonstrate the superiority of the proposed model, which is then deployed online at Taobao and obtain significant improvements in online Click-Through-Rate (CTR) comparing to two baselines.
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UNVERDICTED 2representative citing papers
Introduces STCA for linear-complexity target-to-history attention, RLB for shared user encoding across targets, and length-extrapolative training to enable end-to-end 10K sequence modeling with observed scaling-law gains and production deployment improvements.
citing papers explorer
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FEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
FEDIN improves CTR prediction by using target-aware frequency filtering to isolate low-entropy periodic interest signals from high-entropy noise in user attention patterns.
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Make It Long, Keep It Fast: End-to-End 10K Long User Behavior Sequence Modeling for Billion-Scale Douyin Recommendation
Introduces STCA for linear-complexity target-to-history attention, RLB for shared user encoding across targets, and length-extrapolative training to enable end-to-end 10K sequence modeling with observed scaling-law gains and production deployment improvements.