Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations

• URL: http://arxiv.org/abs/2104.08490v2
• Date: Tue, 20 Apr 2021 01:12:23 GMT
• Title: Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations
• Authors: Pan Li and Alexander Tuzhilin
• Abstract summary: Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. Existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. We propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner.
• Score: 85.6250759280292
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. In addition, they did not consider the duality structure of cross-domain recommendation tasks, thus failing to take into account bidirectional latent relations between users and items and achieve optimal recommendation performance. To address these issues, in this paper we propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Furthermore, we combine the dual learning method with the metric learning approach, which allows us to significantly reduce the required common user overlap across the two domains and leads to even better cross-domain recommendation performance. We test the proposed model on two large-scale industrial datasets and six domain pairs, demonstrating that it consistently and significantly outperforms all the state-of-the-art baselines. We also show that the proposed model works well with very few overlap users to obtain satisfying recommendation performance comparable to the state-of-the-art baselines that use many overlap users.

Related papers

• MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation [63.27390451208503]
  Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance. We propose the Multi-view Disentangled and Adaptive Preference Learning framework. Our framework uses a multiview encoder to capture diverse user preferences.
  arXiv  Detail & Related papers  (2024-10-08T10:06:45Z)
• Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation [71.45854187886088]
  Multi-domain recommender systems benefit from cross-domain representation learning and positive knowledge transfer. We use temporal intra- and inter-domain interactions as contextual information for our method called MAGRec. We perform experiments on publicly available datasets in different scenarios where MAGRec consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-02-12T19:51:32Z)
• Cross-domain recommendation via user interest alignment [20.387327479445773]
  Cross-domain recommendation aims to leverage knowledge from multiple domains to alleviate the data sparsity and cold-start problems in traditional recommender systems. The general practice of this approach is to train user embeddings in each domain separately and then aggregate them in a plain manner. We propose a novel cross-domain recommendation framework, namely COAST, to improve recommendation performance on dual domains.
  arXiv  Detail & Related papers  (2023-01-26T23:54:41Z)
• Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations [92.47380209981348]
  We propose a Diverse Preference Augmentation framework with multiple source domains based on meta-learning. We generate diverse ratings in a new domain of interest to handle overfitting on the case of sparse interactions. These ratings are introduced into the meta-training procedure to learn a preference meta-learner, which produces good generalization ability.
  arXiv  Detail & Related papers  (2022-04-01T10:10:50Z)
• A cross-domain recommender system using deep coupled autoencoders [77.86290991564829]
  Two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors.
  arXiv  Detail & Related papers  (2021-12-08T15:14:26Z)
• RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation [19.61356871656398]
  Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. We propose the RecGURU algorithm framework to generate a Generalized User Representation (GUR) incorporating user information across domains in sequential recommendation.
  arXiv  Detail & Related papers  (2021-11-19T08:41:06Z)
• Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction [76.98616102965023]
  Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem. We propose a novel approach to cross-domain sequential recommendations based on the dual learning mechanism.
  arXiv  Detail & Related papers  (2021-06-05T01:21:21Z)

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.