CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network

• URL: http://arxiv.org/abs/2005.10549v2
• Date: Sat, 23 May 2020 07:13:11 GMT
• Title: CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network
• Authors: Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun
• Abstract summary: We propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN) CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism. On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy.
• Score: 49.35977893592626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In a large recommender system, the products (or items) could be in many different categories or domains. Given two relevant domains (e.g., Book and Movie), users may have interactions with items in one domain but not in the other domain. To the latter, these users are considered as cold-start users. How to effectively transfer users' preferences based on their interactions from one domain to the other relevant domain, is the key issue in cross-domain recommendation. Inspired by the advances made in review-based recommendation, we propose to model user preference transfer at aspect-level derived from reviews. To this end, we propose a cross-domain recommendation framework via aspect transfer network for cold-start users (named CATN). CATN is devised to extract multiple aspects for each user and each item from their review documents, and learn aspect correlations across domains with an attention mechanism. In addition, we further exploit auxiliary reviews from like-minded users to enhance a user's aspect representations. Then, an end-to-end optimization framework is utilized to strengthen the robustness of our model. On real-world datasets, the proposed CATN outperforms SOTA models significantly in terms of rating prediction accuracy. Further analysis shows that our model is able to reveal user aspect connections across domains at a fine level of granularity, making the recommendation explainable.

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)
• Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation [60.09293734134179]
  $textTransition2$ is a novel method to model transitions across both domains and types of user feedback. We introduce a transition-aware graph encoder based on user history, assigning different weights to edges according to the feedback type. We encode the user history using a cross-transition multi-head self-attention, incorporating various masks to distinguish different types of transitions.
  arXiv  Detail & Related papers  (2024-08-15T15:18:55Z)
• Mixed Attention Network for Cross-domain Sequential Recommendation [63.983590953727386]
  We propose a Mixed Attention Network (MAN) with local and global attention modules to extract the domain-specific and cross-domain information. Experimental results on two real-world datasets demonstrate the superiority of our proposed model.
  arXiv  Detail & Related papers  (2023-11-14T16:07:16Z)
• 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)
• Cross-domain User Preference Learning for Cold-start Recommendation [32.83868293457142]
  Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems. It is critical to learn a user's preference from the source domain and transfer it into the target domain. We propose a self-trained Cross-dOmain User Preference LEarning framework, targeting cold-start recommendation with various semantic tags.
  arXiv  Detail & Related papers  (2021-12-07T12:57:05Z)
• 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)
• Personalized Transfer of User Preferences for Cross-domain Recommendation [31.66579257624623]
  How to transfer user's preferences from the source domain to the target domain, is the key issue in Cross-domain Recommendation. We propose a novel framework named Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR) We conduct extensive experiments to evaluate the effectiveness of PTUPCDR on both cold-start and warm-start stages.
  arXiv  Detail & Related papers  (2021-10-21T13:59:18Z)
• Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts [4.945620732698048]
  We describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about the user preferences. The intuition is that user-item interactions in a source domain can augment the recommendation quality in a target domain. We present results on two widely-used datasets - Amazon and Yelp, which support the claim that holistic user preference knowledge leads to better recommendations.
  arXiv  Detail & Related papers  (2021-04-26T18:56:04Z)
• 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)

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.