Robust Representation Learning for Unified Online Top-K Recommendation

• URL: http://arxiv.org/abs/2310.15492v1
• Date: Tue, 24 Oct 2023 03:42:20 GMT
• Title: Robust Representation Learning for Unified Online Top-K Recommendation
• Authors: Minfang Lu, Yuchen Jiang, Huihui Dong, Qi Li, Ziru Xu, Yuanlin Liu, Lixia Wu, Haoyuan Hu, Han Zhu, Yuning Jiang, Jian Xu, Bo Zheng
• Abstract summary: We propose a robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The proposed method has been successfully deployed online to serve real business scenarios.
• Score: 39.12191494863331
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.

Related papers

• Adaptive Conditional Expert Selection Network for Multi-domain Recommendation [10.418133538132635]
  Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) CESAA consists of Conditional Expert Selection (CES) Module and Adaptive Expert Aggregation (AEA) Module. AEA utilizes mutual information loss to strengthen the correlations between experts and specific domains, and significantly improve the distinction between experts.
  arXiv  Detail & Related papers  (2024-11-11T09:39:31Z)
• Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification [32.80872775195836]
  Generalizable vehicle re-identification (ReID) aims to enable the well-trained model in diverse source domains to broadly adapt to unknown target domains. It still faces the challenges of domain shift problem and has difficulty accurately generalizing to unknown target domains. This paper proposes the two-stage Multi-expert Knowledge Confrontation and Collaboration (MiKeCoCo) method.
  arXiv  Detail & Related papers  (2024-07-10T04:06:39Z)
• Multi-Tower Multi-Interest Recommendation with User Representation Repel [0.9867914513513453]
  We propose a novel multi-tower multi-interest framework with user representation repel. Experimental results across multiple large-scale industrial datasets proved the effectiveness and generalizability of our proposed framework.
  arXiv  Detail & Related papers  (2024-03-08T07:36:14Z)
• DIGIC: Domain Generalizable Imitation Learning by Causal Discovery [69.13526582209165]
  Causality has been combined with machine learning to produce robust representations for domain generalization. We make a different attempt by leveraging the demonstration data distribution to discover causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution.
  arXiv  Detail & Related papers  (2024-02-29T07:09:01Z)
• Pre-trained Recommender Systems: A Causal Debiasing Perspective [19.712997823535066]
  We develop a generic recommender that captures universal interaction patterns by training on generic user-item interaction data extracted from different domains. Our empirical studies show that the proposed model could significantly improve the recommendation performance in zero- and few-shot learning settings.
  arXiv  Detail & Related papers  (2023-10-30T03:37:32Z)
• Exploiting Modality-Specific Features For Multi-Modal Manipulation Detection And Grounding [54.49214267905562]
  We construct a transformer-based framework for multi-modal manipulation detection and grounding tasks. Our framework simultaneously explores modality-specific features while preserving the capability for multi-modal alignment. We propose an implicit manipulation query (IMQ) that adaptively aggregates global contextual cues within each modality.
  arXiv  Detail & Related papers  (2023-09-22T06:55:41Z)
• Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment [17.086123737443714]
  Anomaly segmentation plays a pivotal role in identifying atypical objects in images, crucial for hazard detection in autonomous driving systems. While existing methods demonstrate noteworthy results on synthetic data, they often fail to consider the disparity between synthetic and real-world data domains. We introduce the Multi-Granularity Cross-Domain Alignment framework, tailored to harmonize features across domains at both the scene and individual sample levels.
  arXiv  Detail & Related papers  (2023-08-16T22:54:49Z)
• Learn what matters: cross-domain imitation learning with task-relevant embeddings [77.34726150561087]
  We study how an autonomous agent learns to perform a task from demonstrations in a different domain, such as a different environment or different agent. We propose a scalable framework that enables cross-domain imitation learning without access to additional demonstrations or further domain knowledge.
  arXiv  Detail & Related papers  (2022-09-24T21:56:58Z)
• Label Distribution Learning for Generalizable Multi-source Person Re-identification [48.77206888171507]
  Person re-identification (Re-ID) is a critical technique in the video surveillance system. It is difficult to directly apply the supervised model to arbitrary unseen domains. We propose a novel label distribution learning (LDL) method to address the generalizable multi-source person Re-ID task.
  arXiv  Detail & Related papers  (2022-04-12T15:59:10Z)
• Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation [56.694330303488435]
  We propose a Learning to Combine for Multi-Source Domain Adaptation (LtC-MSDA) framework. In the nutshell, a knowledge graph is constructed on the prototypes of various domains to realize the information propagation among semantically adjacent representations. Our approach outperforms existing methods with a remarkable margin.
  arXiv  Detail & Related papers  (2020-07-17T07:52:44Z)

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.