Cross Domain Sentiment Classification Techniques: A Review

In this paper, we present refining graph representation for cross-domain recommendation (CDR) based on edge pruning considering feature distribution in a latent space. Conventional graph-based CDR methods have utilized all ratings and purchase histories of user's products. However, some items purchased by users are not related to the domain for recommendation, and this information becomes noise when making CDR. So, the proposed method introduces edge pruning into the latest graph-based CDR method to refine graph representation. To compare the item embedding features calculated in different domains, we construct a latent space and perform edge pruning through their correlations. Additionally, we introduce a state-of-the-art graph neural network into the graph construction of the proposed method that considers the interactions between users and items thereby obtaining effective embedding features in a domain. This makes it possible to consider domain-specific user preferences and estimate embedding features with high-expressive power. Furthermore, to compare the embedding features of items in the two domains, we construct their latent spaces and project them. Edge pruning is performed using the correlation of items between the two domains on the latent space. We obtain cross domain specific graph representation through edge pruning, which improves the performance by considering the relationship between both items across domains. To the best of our knowledge, no study in the CDR field focuses on eliminating unnecessary node information. We have demonstrated the effectiveness of the proposed method by comparing several graph-based state-of-the-art methods.INDEX TERMS Edge pruning, cross-domain recommendation, latent space graph convolutional networks.

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“…The cross-domain recommendation (CDR) has been studied [13]- [20] to solve the cold-start problem. CDR is a VOLUME x, 2019 FIGURE 1.…”

Section: Introductionmentioning

confidence: 99%

Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

et al. 2022

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“…The problem is called a cold-start problem [2]. To tackle the problem, cross-domain recommendation (CDR) methods, which can effectively recommend items in one domain called a target domain by using information from other domain called a source domain, have been researched [3,4].…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Recommendation Method Based On Multi-Layer Graph Analysis With Visual Information

Hirakawa

Maeda

Ogawa

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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In this paper, a cross-domain recommendation (CDR) method based on multi-layer graph analysis with visual information is presented. Although previous graph-based CDR methods have effectively used users' ratings of products and their purchase histories, they lack essential information such as product images that can have an impact on users' decision of whether to purchase products. Then the proposed method newly introduces visual features obtained from product images into the graph-based CDR. For dealing with visual features in multiple domains, we focus on both intra-domain and interdomain relationships through visual features. Specifically, to obtain effective embedding features from users and items in a domain, the proposed method newly introduces visual features into an optimization process of the latest graph neural network that considers only user-item interactions. Consideration of the visual similarity of items within a domain becomes feasible, and embedding features with high representation ability can be estimated. Furthermore, to consider visual information between domains, we construct multi-layer graphs for each domain and introduce visual features into the training of a feature transformer across these graphs. Therefore, consideration of both intra-domain and inter-domain relationships through visual features contributes to the performance improvement. To our best knowledge, this is the first trial to introduce visual features into multi-layer graph-based CDR. The effectiveness of our method is demonstrated by comparing several state-of-the-art methods.

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Cross Domain Sentiment Classification Techniques: A Review

Cited by 2 publications

References 25 publications

Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

Refining Graph Representation for Cross-Domain Recommendation Based on Edge Pruning in Latent Space

Cross-Domain Recommendation Method Based On Multi-Layer Graph Analysis With Visual Information

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