Cross-domain User Preference Learning for Cold-start Recommendation

• URL: http://arxiv.org/abs/2112.03667v1
• Date: Tue, 7 Dec 2021 12:57:05 GMT
• Title: Cross-domain User Preference Learning for Cold-start Recommendation
• Authors: Huiling Zhou, Jie Liu, Zhikang Li, Jin Yu, Hongxia Yang
• Abstract summary: 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.
• Score: 32.83868293457142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems. Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation. However, when a new domain evolves at its early stage, it has potential users similar to the source domain but with much fewer interactions. It is critical to learn a user's preference from the source domain and transfer it into the target domain, especially on the newly arriving contents with limited user feedback. To bridge this gap, we propose a self-trained Cross-dOmain User Preference LEarning (COUPLE) framework, targeting cold-start recommendation with various semantic tags, such as attributes of items or genres of videos. More specifically, we consider three levels of preferences, including user history, user content and user group to provide reliable recommendation. With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning. Then, a hierarchical memory tree with orthogonal node representation is proposed to further generalize user group preference across domains. The whole framework updates in a contrastive way with a First-In-First-Out (FIFO) queue to obtain more distinctive representations. Extensive experiments on two datasets demonstrate the efficiency of COUPLE in both user and content cold-start situations. By deploying an online A/B test for a week, we show that the Click-Through-Rate (CTR) of COUPLE is superior to other baselines used on Taobao APP. Now the method is serving online for the cross-domain cold micro-video recommendation.

Related papers

• 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)
• 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)
• 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)
• Learning to Learn a Cold-start Sequential Recommender [70.5692886883067]
  The cold-start recommendation is an urgent problem in contemporary online applications. We propose a meta-learning based cold-start sequential recommendation framework called metaCSR. metaCSR holds the ability to learn the common patterns from regular users' behaviors.
  arXiv  Detail & Related papers  (2021-10-18T08:11:24Z)
• 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)
• CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network [49.35977893592626]
  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.
  arXiv  Detail & Related papers  (2020-05-21T10:05:19Z)

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.