Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social Recommendation

Jin, Bo; Ke, Cheng; Zhang, Liang; Fu, Yanjie; Yin, Minghao; Jiang, Lu

doi:10.1145/3340531.3412016

Cited by 11 publications

(3 citation statements)

References 30 publications

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“…To improve the recommendation results, many social recommendation methods have been proposed to incorporate the online social relationships between users into the recommendation framework as side information [21], [24], [25]. Most traditional methods (e.g., Sorec [30], TrustMF [58]) are built based on the matrix factorization architecture to project users into latent factors.…”

Section: A Social-aware Recommendation Methodsmentioning

confidence: 99%

Seismogenic structures of the 2006 ML4.0 Dangan Island earthquake offshore Hong Kong

Xia

Cao

Sun

et al. 2018

J. Ocean Univ. China

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Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.

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Section: A Social-aware Recommendation Methodsmentioning

confidence: 99%

Seismogenic structures of the 2006 ML4.0 Dangan Island earthquake offshore Hong Kong

Xia

Cao

Sun

et al. 2018

J. Ocean Univ. China

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“…In the real world, the relationships between things are often not in pairs, but between two or more entities that together form an interactive relationship [32]. Using simple graphs to represent such non-pairwise relationships would result in information loss [33]. Hypergraphs extend the definition of simple graphs.…”

Section: Social Recommendation Based On Graph Neural Networkmentioning

confidence: 99%

Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

Przystupa

Кочан

2023

Electronics

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Social network information has been widely applied to traditional recommendations that have received significant attention in recent years. Most existing social recommendation models tend to use pairwise relationships to explore potential user preferences, but overlook the complexity of real-life interactions between users and the fact that user relationships may be higher order. These approaches also ignore the dynamic nature of friend influence, which leads the models to treat different friend influences equally in different ways. To address this, we propose a social recommendation algorithm that incorporates graph embedding and higher-order mutual information maximization based on the consideration of social consistency. Specifically, we use the graph attention model to build higher-order information among users for deeper mining of their behavioral patterns on the one hand; while on the other hand, it models user embedding based on the principle of social consistency to finally achieve finer-grained inference of user interests. In addition, to alleviate the problem of losing its own hierarchical information after fusing different levels of hypergraphs, we use self-supervised learning to construct auxiliary branches that fully enhance the rich information in the hypergraph. Experimental results conducted on two publicly available datasets show that the proposed model outperforms state-of-the-art methods.

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“…Instead of performing GNNs on the user-user social graph, researchers have also considered jointly modeling the social diffusion process in the social network and the interest diffusion process in the user-item graph with heterogeneous GNN based models [151], [154], [155], [156], [157], [158], [159]. E.g.,DiffNet++ is proposed to jointly model the interest diffusion from user-item bipartite graph and the influence diffusion from the user-user social graph for user modeling in social recommendation, and have achieved state-of-the-art performance [157].…”

Section: Modeling Social Networkmentioning

confidence: 99%

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

Wu,

He,

Wang

et al. 2021

Preprint

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Influenced by the stunning success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks. In recent years, we have witnessed significant progress in developing neural recommender models, which generalize and surpass traditional recommender models owing to the strong representation power of neural networks. In this survey paper, we conduct a systematic review on neural recommender models, aiming to summarize the field to facilitate future progress. Distinct from existing surveys that categorize existing methods based on the taxonomy of deep learning techniques, we instead summarize the field from the perspective of recommendation modeling, which could be more instructive to researchers and practitioners working on recommender systems. Specifically, we divide the work into three types based on the data they used for recommendation modeling: 1) collaborative filtering models, which leverage the key source of user-item interaction data; 2) content enriched models, which additionally utilize the side information associated with users and items, like user profile and item knowledge graph; and 3) context enriched models, which account for the contextual information associated with an interaction, such as time, location, and the past interactions. After reviewing representative works for each type, we finally discuss some promising directions in this field, including benchmarking recommender systems, graph reasoning based recommendation models, and explainable and fair recommendations for social good.

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Partial Relationship Aware Influence Diffusion via a Multi-channel Encoding Scheme for Social Recommendation

Cited by 11 publications

References 30 publications

Seismogenic structures of the 2006 ML4.0 Dangan Island earthquake offshore Hong Kong

Seismogenic structures of the 2006 ML4.0 Dangan Island earthquake offshore Hong Kong

Social Recommendation Algorithm Based on Self-Supervised Hypergraph Attention

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

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