CausalInt: Causal Inspired Intervention for Multi-Scenario Recommendation

Building appropriate scenarios to meet the personalized demands of different user groups is a common practice. Despite various scenario brings personalized service, it also leads to challenges for the recommendation on multiple scenarios, especially the scenarios with limited traffic. To give desirable recommendation service for all scenarios and reduce the cost of resource consumption, how to leverage the information from multiple scenarios to construct a unified model becomes critical. Unfortunately, the performance of existing multi-scenario recommendation approaches is poor since they introduce unnecessary information from other scenarios to target scenario. In this paper, we show it is possible to selectively utilize the information from different scenarios to construct the scenario-aware estimators in a unified model. Specifically, we first do analysis on multi-scenario modeling with causal graph from the perspective of users and modeling processes, and then propose the Causal Inspired Intervention (CausalInt) framework for multi-scenario recommendation. CausalInt consists of three modules: (1) Invariant Representation Modeling module to squeeze out the scenario-aware information through disentangled representation learning and obtain a scenario-invariant representation; (2) Negative Effects Mitigating module to resolve conflicts between different scenarios and conflicts between scenario-specific and scenario-invariant representations via gradient based orthogonal regularization and model-agnostic meta learning, respectively; (3) Inter-Scenario Transferring module designs a novel TransNet to simulate a counterfactual intervention and effectively fuse the information from other scenarios. Offline experiments over two real-world dataset and online A/B test are conducted to demonstrate the superiority of CausalInt.

[1]  Zhirong Liu,et al.  Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models , 2021, CIKM.

[2]  Zulong Chen,et al.  SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarios , 2021, CIKM.

[3]  Fuzhen Zhuang,et al.  Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising , 2021, KDD.

[4]  Guohui Ling,et al.  Causal Intervention for Leveraging Popularity Bias in Recommendation , 2021, SIGIR.

[5]  Chi Zhang,et al.  One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction , 2021, CIKM.

[6]  Junning Liu,et al.  Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations , 2020, RecSys.

[7]  Yichao Wang,et al.  A Practical Incremental Method to Train Deep CTR Models , 2020, ArXiv.

[8]  Qi Pi,et al.  Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction , 2020, CIKM.

[9]  Mehrdad Farajtabar,et al.  Orthogonal Gradient Descent for Continual Learning , 2019, AISTATS.

[10]  Yike Guo,et al.  Regularizing Deep Multi-Task Networks using Orthogonal Gradients , 2019, ArXiv.

[11]  Zhe Zhao,et al.  Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts , 2018, KDD.

[12]  Mei Wang,et al.  Deep Visual Domain Adaptation: A Survey , 2018, Neurocomputing.

[13]  Guorui Zhou,et al.  Deep Interest Network for Click-Through Rate Prediction , 2017, KDD.

[14]  Yunming Ye,et al.  DeepFM: A Factorization-Machine based Neural Network for CTR Prediction , 2017, IJCAI.

[15]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[16]  Geoffrey E. Hinton,et al.  Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer , 2017, ICLR.

[17]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[18]  Heng-Tze Cheng,et al.  Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.

[19]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[20]  Illtyd Trethowan.  Causality , 1938 .