Cross-Domain Recommendation via Preference Propagation GraphNet

Zhao, Chen; Li, Chenliang; Fu, Cong

doi:10.1145/3357384.3358166

Cited by 89 publications

(48 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al [110] proposed a matrix-based method for cross-domain recommendation. Later, Zhao et al [111] introduced PPGN, which puts users and products from different domains in one graph, and leverages the user-item interaction graph for cross-domain recommendation. Although PPGN outperforms SOTA significantly, the user-item graph contains only interaction relations, and does not consider other relationships among users and items.…”

Section: Future Directionsmentioning

confidence: 99%

A Survey on Knowledge Graph-Based Recommender Systems

Guo

Zhuang

Qin

et al. 2022

IEEE Trans. Knowl. Data Eng.

483

161

View full text Add to dashboard Cite

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

show abstract

Section: Future Directionsmentioning

confidence: 99%

A Survey on Knowledge Graph-Based Recommender Systems

Guo

Zhuang

Qin

et al. 2022

IEEE Trans. Knowl. Data Eng.

483

161

View full text Add to dashboard Cite

show abstract

“…Later, DDTCDR [17] utilizes user information and items' metadata from online platform by using autoencoder, then adopts latent orthogonal mapping to extract user preferences over multiple domains. PPGN [35] adopts graph convolutional network to explore the highorder connectivity between users and items on the joint interaction graph of two domains, and then transfers knowledge by sharing user features. Compared with the shallow cross-domain matrix factorization models, the deep transfer methods generally exhibit better performance, due to their stronger feature extraction ability.…”

Section: Transfer Learning and Cross Domain Collaborative Filteringmentioning

confidence: 99%

“…Surprisingly, PPGN is worse than NGCF and even worse than CoNet. This might be caused by the datasets, since PPGN has been shown to outperform CoNet on CD&Music and Book&Movie in [35]. We guess the poor performance of PPGN on datasets with large distribution gaps might come from that it uses the same propagation layer on the joint graph of the two domains.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…On this basis, some improvements have been proposed [17,19]. For example, CoNet [11] takes neural network as the basic model and uses cross connections unit to improve the learning of matching functions in the current domain, while PPGN [35] extracts more effective common users' features by using GCN on the joint interaction graph of two domains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

Liu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

102

View full text Add to dashboard Cite

Data sparsity is a challenge problem that most modern recommender systems are confronted with. By leveraging the knowledge from relevant domains, the cross-domain recommendation technique can be an effective way of alleviating the data sparsity problem. In this paper, we propose a novel Bi-directional Transfer learning method for cross-domain recommendation by using Graph Collaborative Filtering network as the base model (BiTGCF). BiT-GCF not only exploits the high-order connectivity in user-item graph of single domain through a novel feature propagation layer, but also realizes the two-way transfer of knowledge across two domains by using the common user as the bridge. Moreover, distinct from previous cross-domain collaborative filtering methods, BiTGCF fuses users' common features and domain-specific features during transfer. Experimental results on four couple benchmark datasets verify the effectiveness of BiTGCF over state-of-the-art models in terms of bi-directional cross domain recommendation. CCS CONCEPTS • Information systems → Recommender systems; • Computing methodologies → Transfer learning.

show abstract

“…Specifically, it has been reported that the recommendation performance can be improved by constructing graphs representing both users and items utilizing their interactions. Recent graph-based CDR methods [15][16][17][18] have used data such as user ratings and purchase histories. For instance, preference propagation graphnet [15] utilizes a deep graph-based cross-domain preference matrix to aggregate multi-order user-item interactions (e.g., ratings) from neighborhoods.…”

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)

View full text Add to dashboard Cite

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.

show abstract

Cross-Domain Recommendation via Preference Propagation GraphNet

Cited by 89 publications

References 6 publications

A Survey on Knowledge Graph-Based Recommender Systems

A Survey on Knowledge Graph-Based Recommender Systems

Cross Domain Recommendation via Bi-directional Transfer Graph Collaborative Filtering Networks

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

Contact Info

Product

Resources

About