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

• URL: http://arxiv.org/abs/2302.00302v1
• Date: Wed, 1 Feb 2023 08:08:21 GMT
• Title: A Deep Behavior Path Matching Network for Click-Through Rate Prediction
• Authors: Jian Dong, Yisong Yu, Yapeng Zhang, Yimin Lv, Shuli Wang, Beihong Jin, Yongkang Wang, Xingxing Wang and Dong Wang
• Abstract summary: We propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app. 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. Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model.
• Score: 9.800832176496002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction [8.213386595519928]
  We propose a novel variational inference approach, namely Group Prior Sampler Variational Inference (GPSVI) GPSVI introduces group preferences as priors to refine latent user interests for tail users. Rigorous analysis and extensive experiments demonstrate that GPSVI consistently improves the performance of tail users.
  arXiv  Detail & Related papers  (2024-10-19T13:15:36Z)
• Personalized Behavior-Aware Transformer for Multi-Behavior Sequential Recommendation [25.400756652696895]
  We propose a Personalized Behavior-Aware Transformer framework (PBAT) for Multi-Behavior Sequential Recommendation (MBSR) problem. PBAT develops a personalized behavior pattern generator in the representation layer, which extracts dynamic and discriminative behavior patterns for sequential learning. We conduct experiments on three benchmark datasets and the results demonstrate the effectiveness and interpretability of our framework.
  arXiv  Detail & Related papers  (2024-02-22T12:03:21Z)
• Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction [32.35797628133921]
  Deep Group Interest Network (DGIN) is an end-to-end method to model the user's entire behavior history. DGIN grouping reduces the behavior length significantly, from O(104) to O(102) We identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence.
  arXiv  Detail & Related papers  (2023-11-15T06:36:11Z)
• Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction [97.99938802797377]
  Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in recommender systems. Recent deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. We propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper.
  arXiv  Detail & Related papers  (2022-06-28T03:28:15Z)
• Recommender Transformers with Behavior Pathways [50.842316273120744]
  We build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. We empirically verify the effectiveness of RETR on seven real-world datasets.
  arXiv  Detail & Related papers  (2022-06-13T08:58:37Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [61.114580368455236]
  User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. We propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors.
  arXiv  Detail & Related papers  (2021-09-07T04:28:09Z)
• Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference [71.11416263370823]
  We propose a generative inverse reinforcement learning for user behavioral preference modelling. Our model can automatically learn the rewards from user's actions based on discriminative actor-critic network and Wasserstein GAN.
  arXiv  Detail & Related papers  (2021-05-03T13:14:25Z)
• Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling [70.64257515361972]
  We argue that focusing on tail users could bring more benefits and address the long tails issue. Specifically, we propose a gradient alignment and adopt an adversarial training scheme to facilitate knowledge transfer from the head to the tail.
  arXiv  Detail & Related papers  (2020-10-22T03:12:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.