A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

• URL: http://arxiv.org/abs/2108.03357v1
• Date: Sat, 7 Aug 2021 03:26:16 GMT
• Title: A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
• Authors: Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, Jiadi Yu
• Abstract summary: We propose a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner.
• Score: 27.894300356732696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.

Related papers

• Graph Signal Processing for Cross-Domain Recommendation [37.87497277046321]
  Cross-domain recommendation (CDR) extends conventional recommender systems by leveraging user-item interactions from dense domains to mitigate data sparsity and the cold start problem. Most existing CDR methods suffer from sensitivity to the ratio of overlapping users and intrinsic discrepancy between source and target domains. We propose CGSP, a unified CDR framework based on GSP, which employs a cross-domain similarity graph constructed by flexibly combining target-only similarity and source-bridged similarity.
  arXiv  Detail & Related papers  (2024-07-17T07:52:45Z)
• Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Mining [81.09446228688559]
  Cross-Domain Few-Shots (CD-FSS) poses the challenge of segmenting novel categories from a distinct domain using only limited exemplars. We propose a novel cross-domain fine-tuning strategy that addresses the challenging CD-FSS tasks.
  arXiv  Detail & Related papers  (2024-01-16T14:45:41Z)
• Guided Depth Map Super-resolution: A Survey [88.54731860957804]
  Guided depth map super-resolution (GDSR) aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image. A myriad of novel and effective approaches have been proposed recently, especially with powerful deep learning techniques. This survey is an effort to present a comprehensive survey of recent progress in GDSR.
  arXiv  Detail & Related papers  (2023-02-19T15:43:54Z)
• 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)
• Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation [1.2074552857379273]
  Cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning.
  arXiv  Detail & Related papers  (2021-10-25T05:17:58Z)
• Towards Explainable Scientific Venue Recommendations [0.09668407688201358]
  We propose an unsophisticated method that advances the state-of-the-art in this area. First, we enhance the interpretability of recommendations through non-negative matrix factorization based topic models. Second, we surprisingly can obtain competitive recommendation performance while using simpler learning methods.
  arXiv  Detail & Related papers  (2021-09-21T10:25:26Z)
• 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)
• Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations [85.6250759280292]
  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.
  arXiv  Detail & Related papers  (2021-04-17T09:18:59Z)
• On Evolving Attention Towards Domain Adaptation [110.57454902557767]
  This paper proposes EvoADA: a novel framework to evolve the attention configuration for a given UDA task without human intervention. Experiments on various kinds of cross-domain benchmarks, i.e., Office-31, Office-Home, CUB-Paintings, and Duke-Market-1510, reveal that the proposed EvoADA consistently boosts multiple state-of-the-art domain adaptation approaches.
  arXiv  Detail & Related papers  (2021-03-25T01:50:28Z)
• Cross-Domain Recommendation: Challenges, Progress, and Prospects [21.60393384976869]
  Cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. In this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and future directions.
  arXiv  Detail & Related papers  (2021-03-02T12:58:08Z)

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.