A Graph-based Recommendation across Heterogeneous Domains

Yang, Deqing; He, Jingrui; Huazheng, Qin; Xiao, Yanghua; Wang, Wei

doi:10.1145/2806416.2806523

Cited by 31 publications

(12 citation statements)

References 22 publications

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“…Other methods have since been developed that exploit social network information to assist cross-domain recommender systems. Yang et al used a bipartite graph to represent the relationships between entities across heterogeneous domains and exploit hidden similarity to help recommendations in two domains [111]. Excluding social network information, many user-generated tags in online systems provide auxiliary data for CDRS.…”

Section: Cdrs With Side Informationmentioning

confidence: 99%

Artificial intelligence in recommender systems

Zhang

Jin

2020

Complex Intell. Syst.

198

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Recommender systems provide personalized service support to users by learning their previous behaviors and predicting their current preferences for particular products. Artificial intelligence (AI), particularly computational intelligence and machine learning methods and algorithms, has been naturally applied in the development of recommender systems to improve prediction accuracy and solve data sparsity and cold start problems. This position paper systematically discusses the basic methodologies and prevailing techniques in recommender systems and how AI can effectively improve the technological development and application of recommender systems. The paper not only reviews cutting-edge theoretical and practical contributions, but also identifies current research issues and indicates new research directions. It carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommender systems using AI.

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Section: Cdrs With Side Informationmentioning

confidence: 99%

Artificial intelligence in recommender systems

Zhang

Jin

2020

Complex Intell. Syst.

198

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“…The semantic relationships of items in each domain are also used for knowledge transfer. Yang et al [ 15 ] introduced the tag system into the cross-domain recommendation, and successfully implemented the cold-start problem of recommending, that is, to recommend movies to the new user based on the blog posts on Weibo. The basic idea of the work is to use the semantic relationship between tags on user blog posts and movie tags as a bridge to associate users with movies, and then it predicts user preferences based on graph models.…”

Section: Related Workmentioning

confidence: 99%

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Wang

et al. 2020

Entropy

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Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user’s subjective views, which can reflect the user’s preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas—namely, positive, negative and neutral—by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user’s semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user’s sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.

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“…CMF has its heterogeneous [39] variants, and codebook transfer [27]. The coordinate system transfer can exploit heterogeneous feedbacks [40,57]. Multiple source domains [32] and multi-view learning [13] are also proposed for integrating information from several domains.…”

Section: Related Workmentioning

confidence: 99%

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

Zhang

Yang

2019

The World Wide Web Conference

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Collaborative filtering (CF) is the key technique for recommender systems (RSs). CF exploits user-item behavior interactions (e.g., clicks) only and hence suffers from the data sparsity issue. One research thread is to integrate auxiliary information such as product reviews and news titles, leading to hybrid filtering methods. Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods. In real-world life, no single service can satisfy a user's all information needs. Thus it motivates us to exploit both auxiliary and source information for RSs in this paper. We propose a novel neural model to smoothly enable Transfer Meeting Hybrid (TMH) methods for cross-domain recommendation with unstructured text in an end-to-end manner. TMH attentively extracts useful content from unstructured text via a memory module and selectively transfers knowledge from a source domain via a transfer network. On two real-world datasets, TMH shows better performance in terms of three ranking metrics by comparing with various baselines. We conduct thorough analyses to understand how the text content and transferred knowledge help the proposed model.

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A Graph-based Recommendation across Heterogeneous Domains

Cited by 31 publications

References 22 publications

Artificial intelligence in recommender systems

Artificial intelligence in recommender systems

Cross-Domain Recommendation Based on Sentiment Analysis and Latent Feature Mapping

Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text

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