mlsanityData to 7 Oct 2026

A Deep Behavior Path Matching Network for Click-Through Rate Prediction

arXiv 2302.00302 ↗First posted 1 Feb 2023cs.AIcited by 0

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Abstract (arXiv)

User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user's decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 25 references that are in the map. First posted on arXiv 1 Feb 2023.

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22 of 25 references.

References · 22

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2018Self-Attentive Sequential Recommendationcited by 1,169
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  6. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  7. 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
  8. 2018Deep Interest Evolution Network for Click-Through Rate Predictioncited by 336
  9. 2018xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemscited by 277
  10. 2020Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Predictioncited by 176
  11. 2019Behavior Sequence Transformer for E-commerce Recommendation in Alibabacited by 128
  12. 2019Practice on Long Sequential User Behavior Modeling for Click-Through Rate Predictioncited by 118
10 more
  1. 2018Learning Tree-based Deep Model for Recommender Systemscited by 99
  2. 2022Intent Contrastive Learning for Sequential Recommendationcited by 90
  3. 2019Deep Session Interest Network for Click-Through Rate Predictioncited by 64
  4. 2021End-to-End User Behavior Retrieval in Click-Through RatePrediction Modelcited by 45
  5. 2021Deep Learning for Click-Through Rate Estimationcited by 33
  6. 2021FeedRec: News Feed Recommendation with Various User Feedbackscited by 23
  7. 2022Disentangling Long and Short-Term Interests for Recommendationcited by 22
  8. 2021Graph Intention Network for Click-through Rate Prediction in Sponsored Searchcited by 14
  9. 2019Operation-aware Neural Networks for User Response Predictioncited by 10
  10. 2023Decision-Making Context Interaction Network for Click-Through Rate Predictioncited by 4

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