DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation

Abstract

Social recommendation has emerged to leverage social connections among users for predicting users' unknown preferences, which could alleviate the data sparsity issue in collaborative filtering based recommendation. Early approaches relied on utilizing each user's first-order social neighbors' interests for better user modeling, and failed to model the social influence diffusion process from the global social network structure. Recently, we propose a preliminary work of a neural influence Diff usion Network (i.e., DiffNet) for social recommendation [37]. DiffNet models the recursive social diffusion process for each user, such that the influence diffusion hidden in the higher-order social network is captured in the user embedding process. Despite the superior performance of DiffNet, we argue that, as users play a central role in both user-user social network and user-item interest network, only modeling the influence diffusion process in the social network would neglect the latent collaborative interests of users hidden in the user-item interest network. To this end, in this paper, we propose DiffNet++, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework. By reformulating the social recommendation as a heterogeneous graph with social network and interest network as input, DiffNet++ advances DiffNet by injecting both the higher-order user latent interest reflected in the user-item graph and higher-order user influence reflected in the user-user graph for user embedding learning. This is achieved by iteratively aggregating each user's embedding from three aspects: the user's previous embedding, the influence aggregation of social neighbors from the social network, and the interest aggregation of item neighbors from the user-item interest network. Furthermore, we design a multi-level attention network that learns how to attentively aggregate user embeddings from these three aspects. Finally, extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

“…• GraphRec [9] is the first GNN-based social recommendation model that models both user-item and user-user interactions. • DiffNet++ [40] is the latest GCN-based social recommendation method that models the recursive dynamic social diffusion in both the user and item spaces. • DHCF [16] is a recent hypergraph convolutional network-based method that models the high-order correlations among users and items for general recommendation.…”

“…GraphRec [9] is the first to introduce GNNs to social recommendation by modeling the user-item and user-user interactions as graph data. DiffNet [41] and its extension DiffNet++ [40] model the recursive dynamic social diffusion in social recommendation with a layer-wise propagation structure. Wu et al [42] propose a dual graph attention network to collaboratively learn representations for two-fold social effects.…”

“…Recently, graph neural networks (GNNs) [43] have achieved great success in a wide range of areas. Owing to their powerful capability in modeling relational data, GNNs-based models also have shown prominent performance in social recommendation [9,19,[40][41][42]58]. However, a key limitation of these GNNs-based social recommendation models is that they only exploit the simple pairwise user relations and ignore the ubiquitous high-order relations among users.…”

Graph Learning based Recommender Systems: A Review

“…They have been widely adopted to help the users of many popular content sharing and e-Commerce web sites to more easily find relevant content, products or services. Meanwhile, Graph Learning (GL), which relates to machine learning applied to graph structure data, is an emerging technique of AI which is rapidly developing and has shown its great capability in recent years [Wu et al, 2021]. In fact, by benefiting from these capabilities to learn relational data, an emerging RS paradigm built on GL, i.e., Graph Learning based Recommender Systems (GLRS), has been proposed and studied extensively in the last few years .…”

“…As one of the most promising machine learning techniques, GL has shown great potential in deriving knowledge embedded in different kinds of graphs. Specifically, many GL techniques, such as random walk and graph neural networks, have been developed to learn the particular type of relations modeled by graphs, and have demonstrated to be quite effective [Wu et al, 2021]. Consequently, employing GL to model various relations in RS is a natural and compelling choice.…”