Robust Preference-Guided Denoising for Graph based Social Recommendation

Social recommendations introduce additional social information to capture users’ potential item preferences, thereby providing more accurate recommendations. However, friends do not always have the same or similar preferences, which means that social information is redundant and often biased for useritem interaction network. In addition, current social recommendation models focus on the item-level preferences, neglecting the critical fine-grained preference influence factors. To address these issues, we propose the Robust Preference-Guided based Disentangled Graph Social Recommendation (RPGD). First, we employ a graph neural network to adaptively convert the social network into a social preference network based on social information and user-item interaction information, reducing bias between social relationships and preference relationships. Then, we propose a self-supervised learning method that utilizes the social network to constrain and optimize the social preference network, thereby enhancing the stability of the network. Finally, we propose a method for disentangled preference representation to explore fine-grained preference influence factors, that enhance the performance of user and item representations. We conducted experiments on some open-source real-world datasets, and the results show that RPGD outperforms the SOTA performance on social recommendations. Our code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Andrewsama/RPGD-master</uri> .

Recent years, people have been gradually influenced by online socialization. The emergence and development of Graph Neural Networks (GNN) has shown great advantages in mining implicit data and expressing node relationships, etc. However, due to the arbitrary nature of establishing neighborly relationships, the judging reliability of trusting relationships between neighbors is a difficult issue. Therefore, this paper proposes an innovative approach to obtain the valid neighbor relationships based on real user-item interactions and friend trust relationships in the dataset. The proposed method can address the impact of invalid neighbors on the social model and achieve the effect of enhancing neighbor perception. For neighbor interaction in the graph neural network model, this paper establishes the direct connection between users and items through mapping of multilayer perceptron firstly. Then it integrates neighbor similarity and neighbor sampling to mitigate the interference information of invalid neighbors and achieves the effect of enhancing the perceived information of neighbor interaction. Finally, this paper establishes the item social space and user social space with enhanced neighbor perception according to the dyadic nature and organically integrated them to enrich the social data. Comparison experiments are carried out based on two publicly available datasets Epinions and Ciao, the recommended method performs better than other social recommendation models with the MAE and RMSE values being improved by 0.81%-1.09% and 1.15%-1.41% respectively.

GNN-based social recommendation aims to use social network information to improve recommendation performance of traditional user–item interaction network (U–I network). However, in graph neural network information aggregation, both social networks and U–I networks inevitably have noise, which affects accuracy of recommendation results. To reduce the noise impact of network data, we propose Robust Social Recommendation based on Contrastive Learning and Dual-Stage Graph Neural Network (CLDS). First, considering instability of social networks, we propose the social preference network. It is robust and retains only social friend relationships with common preferences. Based on it and U–I network, we construct a social recommendation pre-training model. Next, we propose self-contrastive learning method. The method initializes multiple social network node representations through Gaussian distribution, pre-training and random disturbance, respectively. Then, it uses contrastive learning on the generated multiple node representations to enhance the robustness of node representation. Finally, CLDS avoids directly capturing potentially user–user and item–item information in U–I networks which is incomplete and untrusted. And instead, it only extracts user–item information to reduce the noise generated by GNN-based U–I network information aggregation. We conduct experiments under the open-source real network dataset. The experimental results show that CLDS outperforms state-of-art methods in social recommendation. The code is available at: https://github.com/Andrewsama/CLDS-master.

An enhanced attentive implicit relation embedding for social recommendation

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.In this paper, we develop a joint personal and social latent factor (PSLF) model for social recommendation. Specifically, it combines the state-of-the-art collaborative filtering and the social network modeling approaches for social recommendation. Especially, the PSLF extracts the social factor vectors for each user based on the state-of-the-art mixture membership stochastic blockmodel, which can explicitly express the varieties of the social relationship. To optimize the PSLF model, we develop a scalable expectation-maximization (EM) algorithm, which utilizes a novel approximate mean-field technique for fast expectation computation. We compare our approach with the latest social recommendation approaches on two real datasets, Flixter and Douban (both with large social networks). With similar training cost, our approach has shown a significant improvement in terms of prediction accuracy criteria over the existing approaches.

Social network information has been proven to be beneficial to improve the recommendation accuracy. Some recent works show a user may trust different subsets of friends regarding different domains concerning the heterogeneity and diversity of social relationships. However, these works obtain the friends subsets mainly by dividing friendships depending on the item categories, which aggravate the sparsity problem of social relationships and limit the contribution of social recommendation. In order to solve the issue, in this paper, we propose a novel social recommendation model by incorporating the friendships from different clustering-based user groups. We first formalize the user-preference matrix which describes the preferences of users from multiple domains and obtain the user groups by using the PDSFCM (Partial Distance Strategy Fuzzy C-Means) algorithm. Then we define the clustering-specific friends subsets and design a clustering-based social regularization term to integrate these friendships into the traditional Matrix Factorization model. The comparison experiments on Epinions data set demonstrate that our approach outperforms other state-of-the-art methods in terms of RMSE and MAE.KeywordsSocial RecommendationFuzzy C-MeansClustering-based Social RegularizationMatrix Factorization

Social recommendation with Graph Neural Networks(GNNs) learns to represent cold users by fusing user-user social relations with user-item interactions, thereby alleviating the cold-start problem associated with recommender systems. Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias. Contrastive learning helps resolve this dilemma by identifying the properties that distinguish positive from negative samples. In its previous combinations with recommender systems, social relationships and cold-start cases in this context are not considered. Also, they primarily focus on collaborative features between users and items, leaving the similarity between items under-utilized. In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. To take full advantage of social relations, we create dynamic node embeddings for each user by aggregating information from different neighbors according to each different query item, in the form of user-item pairs. We further design a dual-branch self-supervised contrastive objective to account for user-item collaborative features and item-item mutual information, respectively. On one hand, our framework eliminates popularity bias with proper negative sampling in contrastive learning, without extra ground-truth supervision. On the other hand, we extend previous contrastive learning methods to provide a solution to cold-start problem with social relations included. Extensive experiments on two real-world social recommendation datasets demonstrate its effectiveness.

A survey of graph neural network based recommendation in social networks

In this paper, we propose SIR, a Social Item Recommendation model based on latent variable model and neighborhood model which effectively models the user interest similarities and social relationships in online social networks. We develop the learning algorithm for the parameter estimates of SIR. Furthermore, we construct an extended SIR model (SIR+) by taking the social interaction features into account to improve the performance of top-A item recommendation. The experiments on a real dataset from Sina Weibo, one of the most popular social network sites (SNS) in China, demonstrate that both SIR and SIR+ outperform the traditional collaborative filtering methods, and SIR+ achieves a better performance than SIR.

A Social-aware Gaussian Pre-trained model for effective cold-start recommendation

Nowadays, the issue of information overload is gradually gaining exposure in the Internet of Things (IoT), calling for more research on recommender system in advance for industrial IoT scenarios. With the ever-increasing prevalence of various social networks, social recommendations (SoR) will certainly become an integral application that provides more feasibly personalized information service for future IoT users. However, almost all of the existing research managed to explore and quantify correlations between user preferences and social relationships, while neglecting the correlations among item features which could further influence the topologies of some social groups. To tackle with this challenge, in this article, a deep graph neural network-based social recommendation framework (GNN-SoR) is proposed for future IoTs. First, user and item feature spaces are abstracted as two graph networks and respectively encoded via the graph neural network method. Next, two encoded spaces are embedded into two latent factors of matrix factorization to complete missing rating values in a user-item rating matrix. Finally, a large amount of experiments are conducted on three real-world data sets to verify the efficiency and stability of the proposed GNN-SoR.

The influence of social relationships has received considerable attention in recommendation systems. In this paper, we propose a personalized advertisement recommendation system based on user preference and social network information. The proposed system uses collaborative filtering and frequent pattern network techniques using social network information to recommend personalized advertisements. Frequent pattern network is employed to alleviate cold-start and sparsity problems of collaborative filtering. For the social relationship modeling, direct and indirect relations are considered and relation weight between users is calculated by using six degrees of Kevin Bacon. Weight `1' is given to those who have connections directly, and weight `0' is given to those who are over six steps away and hove no relation to each other. According to a research of Kevin Bacon, everybody can know certain people through six depths of people. In order to improve prediction accuracy, we apply social relationship to user modeling. In our experiments, advertisement information is collected and item rating and user information including social relations are extracted from a social network service. The proposed system applies user modeling between collaborative filtering and frequent pattern network model to recommend advertisements according to user condition. User's types are composed with combinations of both techniques. We compare the performance of the proposed method with that of other methods. From the experimental results, a proposed system applying user modeling using social relationships can achieve better performance and recommendation quality than other recommendation systems.

Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.

A service-recommendation method for the Internet of Things leveraging implicit social relationships

Recommender systems based on multiple social networks correlation