Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce

• URL: http://arxiv.org/abs/2012.08952v1
• Date: Wed, 16 Dec 2020 13:52:14 GMT
• Title: Scenario-aware and Mutual-based approach for Multi-scenario Recommendation in E-Commerce
• Authors: Yuting Chen, Yanshi Wang, Yabo Ni, An-Xiang Zeng and Lanfen Lin
• Abstract summary: How to make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. We propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios.
• Score: 12.794276204716642
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems (RSs) are essential for e-commerce platforms to help meet the enormous needs of users. How to capture user interests and make accurate recommendations for users in heterogeneous e-commerce scenarios is still a continuous research topic. However, most existing studies overlook the intrinsic association of the scenarios: the log data collected from platforms can be naturally divided into different scenarios (e.g., country, city, culture). We observed that the scenarios are heterogeneous because of the huge differences among them. Therefore, a unified model is difficult to effectively capture complex correlations (e.g., differences and similarities) between multiple scenarios thus seriously reducing the accuracy of recommendation results. In this paper, we target the problem of multi-scenario recommendation in e-commerce, and propose a novel recommendation model named Scenario-aware Mutual Learning (SAML) that leverages the differences and similarities between multiple scenarios. We first introduce scenario-aware feature representation, which transforms the embedding and attention modules to map the features into both global and scenario-specific subspace in parallel. Then we introduce an auxiliary network to model the shared knowledge across all scenarios, and use a multi-branch network to model differences among specific scenarios. Finally, we employ a novel mutual unit to adaptively learn the similarity between various scenarios and incorporate it into multi-branch network. We conduct extensive experiments on both public and industrial datasets, empirical results show that SAML consistently and significantly outperforms state-of-the-art methods.

Related papers

• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation [11.489766641148151]
  Multi-scenario Recommendation (MSR) is an important topic that improves ranking performance by leveraging information from different scenarios. Recent methods for MSR mostly construct scenario shared or specific modules to model commonalities and differences among scenarios. We propose a unified model named Cross-Scenario Information Interaction (CSII) to serve all scenarios by a mixture of scenario-dominated experts.
  arXiv  Detail & Related papers  (2024-07-29T06:17:33Z)
• A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce [13.991015845541257]
  We propose an effective and universal framework for Unified Search and Recommendation (USR) USR can be applied to various multi-scenario models and significantly improve their performance. USR has been successfully deployed in the 7Fresh App.
  arXiv  Detail & Related papers  (2024-05-17T14:57:52Z)
• DSFNet: Learning Disentangled Scenario Factorization for Multi-Scenario Route Ranking [19.448901545665212]
  Multi-scenario route ranking (MSRR) is crucial in many industrial mapping systems. We propose a novel method, Disentangled Scenario Factorization Network (DSFNet), which flexibly composes scenario-dependent parameters. We also propose MSDR, the first large-scale publicly available annotated industrial Multi-Scenario Driving Route dataset.
  arXiv  Detail & Related papers  (2024-03-30T04:39:18Z)
• Multi-Scenario Ranking with Adaptive Feature Learning [47.03869915754652]
  Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry. MSL produces different paradigms by searching more optimal network structure. Our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.
  arXiv  Detail & Related papers  (2023-06-29T07:14:34Z)
• UMSE: Unified Multi-scenario Summarization Evaluation [52.60867881867428]
  Summarization quality evaluation is a non-trivial task in text summarization. We propose Unified Multi-scenario Summarization Evaluation Model (UMSE) Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios.
  arXiv  Detail & Related papers  (2023-05-26T12:54:44Z)
• HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction [50.40732146978222]
  Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications. We propose a Hierarchical information extraction Network (HiNet) for multi-scenario and multi-task recommendation. HiNet achieves a new state-of-the-art performance and significantly outperforms existing solutions.
  arXiv  Detail & Related papers  (2023-03-10T17:24:41Z)
• Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation [35.4495536683099]
  We propose a scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests.
  arXiv  Detail & Related papers  (2022-08-24T11:44:00Z)
• Variational Distillation for Multi-View Learning [104.17551354374821]
  We design several variational information bottlenecks to exploit two key characteristics for multi-view representation learning. Under rigorously theoretical guarantee, our approach enables IB to grasp the intrinsic correlation between observations and semantic labels.
  arXiv  Detail & Related papers  (2022-06-20T03:09:46Z)
• SAR-Net: A Scenario-Aware Ranking Network for PersonalizedFair Recommendation in Hundreds of Travel Scenarios [11.603889783538309]
  We propose a novel Scenario-Aware Ranking Network (SAR-Net) to address these issues. Experiments on an offline dataset covering over 80 million users and 1.55 million travel items and an online A/B test demonstrate the effectiveness of our SAR-Net.
  arXiv  Detail & Related papers  (2021-10-13T03:49:45Z)
• Variational Inference for Deep Probabilistic Canonical Correlation Analysis [49.36636239154184]
  We propose a deep probabilistic multi-view model that is composed of a linear multi-view layer and deep generative networks as observation models. An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer. A generalization to models with arbitrary number of views is also proposed.
  arXiv  Detail & Related papers  (2020-03-09T17:51:15Z)

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.