Social Trust Relationships Research Articles

A mobile services recommendation system fuses implicit and explicit user trust relationships

In recent years, with the development of advanced mobile applications, people’s various daily behavior data, such as geographic location, social information, hobbies, are more easily collected. To process these data, data cross-boundary fusion has become a key technology, and there are some challenges, such as solving the problems of the cross-boundary business integrity, cross-boundary value complementarity and so on. Mobile Services Recommendation requires improved recommendation accuracy. User trust is an effective measure of information similarity between users. Using trust can effectively improve the accuracy of recommendations. The existing methods have low utilization of general trust data, sparseness of trust data, and lack of user trust characteristics. Therefore, a method needs to be proposed to make up for the shortcomings of explicit trust relationships and improve the accuracy of user interest feature completion. In this paper, a recommendation model is proposed to mine the implicit trust relationships from user data and integrate the explicit social information of users. First, the rating prediction model was improved using the traditional Singular Value Decomposition (SVD) model, and the implicit trust relationships were mined from the user’s historical data. Then, they were fused with the explicit social trust relationships to obtain a crossover data fusion model. We tested the model using three different orders of magnitude. We compared the user preference prediction accuracies of two models: one that does not integrate social information and one that integrates social information. The results show that our model improves the user preference prediction accuracy and has higher accuracy for cold start users. On the three data sets, the average error is reduced by 2.29%, 5.44% and 4.42%, suggesting that it is an effective data crossover fusion technology.

The Experiential Basis of Social Trust Towards Ethnic Outgroup Members

This study contributes to social trust research by examining the extent to which cross-group interaction provokes exclusionary reactions among trusters. Specifically, we examine whether unpleasant contact with ethnic outgroup members constrains the relationship between social trust and ethnic exclusionism among majority members. The analysis shows that: (a) social trust relates negatively to ethnic exclusionism, (b) unpleasant contact experiences relate positively to ethnic exclusionism, and (c) social trust is almost unrelated to ethnic exclusionism when contact experiences have been unpleasant. Inconsistent with “moralistic” perspectives, social trusters’ views of ethnic outgroup members are remarkably experience-based. To understand the “experiential effect”, we develop a tentative interpretation emphasizing the joint capacity of negative emotions and group membership salience to enhance the implications of unpleasant encounters among contacted trusters. The analysis is based on the 2014-European Social Survey, including 27,796 individuals and 21 countries. The concluding section discusses how our affect-salience interpretation adds to the experiential understanding of social trust and interethnic relations in contemporary nations.

A trust-enhanced and preference-aware collaborative method for recommending new energy vehicle.

New energy vehicle (NEV), an Eco-friendly innovation to alleviate the problems of energy scarcity and environmental pollution, is increasingly popular in many countries. Various new energy vehicles are provided with quantity of basic information (e.g., performance, quality, and price), which hinders potential users from effectively finding the most desired or interested new energy vehicles to satisfy their personalized requirements. This paper proposes a three-stage recommendation method for facilitating users to find the proper NEV considering users' preferences and social trust relationship. In the first stage, the users' preferences on evaluation criteria are determined by best-worst method (BWM) through hesitant fuzzy preference comparison vectors. In the second stage, the users' demographic similarity is obtained considering different formats of information, and then users' trust degrees are generated from the entire propagation paths using n dimensional path-ordering-induced order-weighted averaging (NP-IOWA) operator, thereby obtaining the trust-based similarity. In the third stage, the comprehensive user-rating matrix is constructed with the obtained weights, and then, it is combined with the trust-based similarity to recommend NEV based on collaborative filtering technique. A case study is given to illustrate the feasibility of the proposed method and the comparative analysis is conducted to show the advantages of the proposed method.

Multi-fuzzy-objective graph pattern matching in big graph environments with reliability, trust and social relationship

With the advent of the era of big data, the scale of data has grown dramatically, and there is a close correlation between massive multi-source heterogeneous data, which can be visually depicted by a big graph. Big graph, especially from Web data, social networks, or biometric data, has attracted more and more attention from researchers, which usually contains complex relationships and multiple attributes. How to perform efficient query and matching on big graph data is the basic problem on analyzing big graph. Using multi-constrained graph pattern matching, we can design patterns that meet our specific requirements, and find matched subgraphs to locate the required patterns to accomplish specific tasks. So how to find matched subgraphs with good attributes in big graph becomes the key problem on big graph research. Considering the possibility that a node in a subgraph may fail due to reliability, in order to select more and better matched subgraphs, in this paper, we introduce fuzziness and reliability into multi-objective graph pattern matching, and then use a multi-objective genetic algorithm NSGA-II to find the subgraphs with higher reliability and better attributes including social trust and social relationship. Finally, a reliability-based multi-fuzzy-objective graph pattern matching method (named as RMFO-GPM) is proposed. The experimental results on real data sets show the effectiveness of the proposed RMFO-GPM method comparing with other state-of-art methods.

An Optimization-Based Approach to Social Network Group Decision Making with an Application to Earthquake Shelter-Site Selection.

The social network has emerged as an essential component in group decision making (GDM) problems. Thus, this paper investigates the social network GDM (SNGDM) problem and assumes that decision makers offer their preferences utilizing additive preference relations (also called fuzzy preference relations). An optimization-based approach is devised to generate the weights of decision makers by combining two reliable resources: in-degree centrality indexes and consistency indexes. Based on the obtained weights of decision makers, the individual additive preference relations are aggregated into a collective additive preference relation. Further, the alternatives are ranked from best to worst according to the obtained collective additive preference relation. Moreover, earthquakes have occurred frequently around the world in recent years, causing great loss of life and property. Earthquake shelters offer safety, security, climate protection, and resistance to disease and ill health and are thus vital for disaster-affected people. Selection of a suitable site for locating shelters from potential alternatives is of critical importance, which can be seen as a GDM problem. When selecting a suitable earthquake shelter-site, the social trust relationships among disaster management experts should not be ignored. To this end, the proposed SNGDM model is applied to evaluate and select earthquake shelter-sites to show its effectiveness. In summary, this paper constructs a novel GDM framework by taking the social trust relationship into account, which can provide a scientific basis for public emergency management in the major disasters field.

BayesTrust and VehicleRank: Constructing an Implicit Web of Trust in VANET

As Vehicular Ad hoc Network (VANET) features random topology and accommodates freely connected nodes, it is important that the cooperation among the nodes exists. This paper proposes a trust model called Implicit Web of Trust in VANET (IWOT-V) to reason out the trustworthiness of vehicles. Such that untrusted nodes can be identified and avoided when we make a decision regarding whom to follow or cooperate with. Furthermore, the performance of Cooperative Intelligent Transport System (C-ITS) applications improves. The idea of IWOT-V is mainly inspired by web page ranking algorithms such as PageRank. Although there does not exist explicit link structure in VANET because of random topology and dynamic connections, social trust relationship among vehicles exists and an implicit web of trust can be derived. To accomplish the derivation, two algorithms are presented, i.e., BayesTrust and VehicleRank. They are responsible for deriving the local and global trust relationships, respectively. The simulation results show that IWOT-V can accurately identify trusted and untrusted nodes if enough local trust information is collected. The performance of IWOT-V affected by five threat models is demonstrated, and the related discussions are also given.

TCFACO: Trust-aware collaborative filtering method based on ant colony optimization

Recommender systems (RSs) aim to help users to find relevant information based on their preferences instead of searching through extensive volume of information using search engines. Accurate prediction of unknown ratings is one of the key challenges in the analysis of RSs. Collaborative Filtering (CF) is a well-known recommendation method that estimates missing ratings by employing a set of similar users to the target user. An outstanding topic in CF is picking out an appropriate set of users and using them in the rating prediction process. In this paper, a novel CF method is proposed to predict missing ratings accurately. The proposed method called TCFACO uses trust statements as a rich side information with Ant Colony Optimization (ACO) method. TCFACO consists of three main steps. In the first step, users are ranked considering available rating values and social trust relationships. Then, in the second step, the ACO method is utilized to assign proper weight values to users to show how they are similar to the target user. A set of top similar users is filter out in the third step to be used in predicting unknown ratings for the target user. In other words, to speed up identifying similar users, the proposed method first filters out a majority part of dissimilar users and then runs the ACO on only a reduced set of users to weight them. Several experiments were performed on three real-world datasets to evaluate the effectiveness of the proposed method and the results show that the proposed method performs better than the state-of-the-art methods.

A novel application recommendation method combining social relationship and trust relationship for future internet of things

Traditional collaborative filtering methods always utilize Cosine and Pearson methods to calculate the similarity of users. When the nearest neighbor doesn’t comment the predicted item, then the nearest neighbor has no influence on results, thus affecting the accuracy of collaborative filtering recommendation. And the traditional recommendation systems always have the problems of data sparsity, cold start and so on. In this paper, we consider social relationship and trust relationship, and put forward a novel application recommendation method that combines users’ social relationship and trust relationship. Specifically, we combine social relationship and user preference towards applications to calculate similarity score, we fuse the trust relationship based on familiarity and user reputation to calculate trust score. The final prediction score is calculated by fusing similar relationship and trust relationship properly. And the proposed method can effectively improve accuracy of recommendations.

A trust-based recommendation method using network diffusion processes

A variety of rating-based recommendation methods have been extensively studied including the well-known collaborative filtering approaches and some network diffusion-based methods, however, social trust relations are not sufficiently considered when making recommendations. In this paper, we contribute to the literature by proposing a trust-based recommendation method, named CosRA+T, after integrating the information of trust relations into the resource-redistribution process. Specifically, a tunable parameter is used to scale the resources received by trusted users before the redistribution back to the objects. Interestingly, we find an optimal scaling parameter for the proposed CosRA+T method to achieve its best recommendation accuracy, and the optimal value seems to be universal under several evaluation metrics across different datasets. Moreover, results of extensive experiments on the two real-world rating datasets with trust relations, Epinions and FriendFeed, suggest that CosRA+T has a remarkable improvement in overall accuracy, diversity and novelty. Our work takes a step towards designing better recommendation algorithms by employing multiple resources of social network information.

Manifestation of Compensatory Behavior among Preschool Children with Trust Deprivation

The article considers the problem of personal features formation among preschoolers with deprivation. The article presents the results of research aimed to reveal the peculiarities of the mental deprivation and trust deprivation among orphans, and to compare the results with the children raised in families. Many basic attitudes of the orphans are deformed, in particular, social trust relationships with the world, which are manifested in two forms of compensatory behavior. The first group comprises children with trust deprivation of passive type, characterized by anxiety, shyness, hypochondria, inability to stand up for themselves. The second group comprises children with trust deprivation of active type; these children are outwardly sociable, but they are prone to aggressive reactions, antisocial behavior and conflicts. Despite the difference in the behavior, orphans are characterized by common personal features: insecurity, inferiority, hostility, proneness to conflict, difficulties in communication.

Leveraging Transitive Trust Relations to Improve Cross-Domain Recommendation

Cross-domain recommendation has become increasingly popular because it can determine dependencies and correlations among different domains. However, this type of recommendation still suffers from data sparsity limitations. Social trust relationship helps alleviate this problem. Existing cross-domain recommendation algorithms focus on modeling user behavior in different domains but disregard the social trust relationship among users. In this paper, we propose a transitive trust-aware cross-domain recommendation model that incorporates the context dependence and transitivity of social trust relations. First, we construct a different transitive trust network for each single domain. Then, we develop a novel probabilistic matrix factorization model for each domain that utilizes the transitive trust-aware model to mine social trust. Finally, we present a nonlinear user–vector mapping algorithm to bridge the feedback of different domains. Experimental results indicate that our method significantly outperforms several state-of-the-art methods, produces higher rating prediction accuracy, and exhibits better recommendation performance on several real-world recommendation tasks.

Social capital and health in China: exploring the mediating role of lifestyle

BackgroundAlthough social capital as a key determinant of health has been well established in various studies, little is known about how lifestyle factors mediate this relationship. Understanding the cross-relationships between social capital, health, and lifestyle factors is important if health promotion policies are to be effective. The purpose of this study is to explore whether different dimensions of social capital and lifestyle factors are related, and whether lifestyle factors mediate the association between social capital and self-rated health (SRH) and psychological well-being (PWB) in China.MethodsThis study used nationally representative data from the 2014 China Family Panel Studies (n = 28,916). The data reported on three dimensions of individual-level social capital: social trust, social relationship and Chinese Communist Party (CCP) membership. Health was assessed using SRH and PWB. Five lifestyle indicators were recorded: healthy diet, physical activity, smoking, sleeping, and non-overweight status. Logistic regression was used to examine the associations between social capital and lifestyle factors, and whether there was a mediating role of lifestyle. Odds ratios relating health status to social capital were reported before and after adjustment for lifestyle factors. Mediation analysis was then used to calculate the total, direct and indirect effects of social capital on SRH and PWB.ResultsThe results show that social trust was significantly associated with all five lifestyle factors. Social relationship was significantly associated with four of the five: healthy diet, physical activity, sleeping and non-overweight. CCP membership was only significantly associated with two lifestyle factors: physical activity and non-overweight. Social trust and social relationship were significantly related to both SRH and PWB. CCP membership was only significantly related to SRH. Mediation analysis found modest evidence that lifestyle factors influenced the relationship between all three types of social capital and SRH. In contrast, only social trust and social relationship, but not CCP membership, were mediated by lifestyle factors with respect to PWB.ConclusionsThis study is the first to explore the mediating role of lifestyle factors in the relationship between social capital and health in China. The overall findings suggest that lifestyle factors modestly mediate the association between social capital and health. The degree of mediating effect varies across different dimensions of social capital. Social capital-based health promotion policies would benefit from taking lifestyle factors into account.

ARMOR: A trust-based privacy-preserving framework for decentralized friend recommendation in online social networks

Friend recommendation in online social networks (OSNs) has recently experienced rapid development and received much research attention. Existing recommender systems on the basis of the big social data mostly employ centralized framework, which would cause lots of problems, such as single point failure, communication bottleneck and so on. Some other studies focus on decentralized framework for recommendation, however, most of them concentrate on the improvement of recommendation quality, while underestimating privacy issues, e.g. OSN users’ privacy concerns regarding their social relationships, social attributes, and recommendation profiles. In this paper, we propose a novel decentralized framework, namely ARMOR, which utilizes OSN users’ social attributes and trust relationships to achieve the friend recommendation in a privacy-preserving manner. In ARMOR, we adopt a light-weight privacy-preserving protocol to aggregate the utilities of multi-hop trust chains and compute the recommender results securely. We also analyze the efficiency of ARMOR in theory and prove that OSN users’ privacy can be preserved. Finally, we conduct an experiment to evaluate ARMOR over a real-world dataset and empirical results demonstrate that our ARMOR can effectively and efficiently recommend friends in a privacy-preserving way.

Research of social recommendation based on social tag and trust relation

As for the data sparsity and cool boot problem, this paper brings forward a social network recommendation means combined with social tags and trust relations. It collects major information relating to social trust relations, item tag information and user rating matrix based on probabilistic matrix factorization. All the data resources from different dimensions are connected through shared users potential spaces (or item potential spaces). The above mentioned two types of spaces can be obtained by probabilistic matrix factorization.In this way, effective social recommendation means can be achieved. The results generated from Epinions and Movielens experiments reveal that the proposed algorithm is superior to the existing Trust-based Social Recommendation or Social Tag Recommendation especially for active users with only a few rating records.

Factored similarity models with social trust for top-N item recommendation

Trust-aware recommender systems have attracted much attention recently due to the prevalence of social networks. However, most existing trust-based approaches are designed for the recommendation task of rating prediction. Only few trust-aware methods have attempted to recommend users an ordered list of interesting items, i.e., item recommendation. In this article, we propose three factored similarity models with the incorporation of social trust for item recommendation based on implicit user feedback. Specifically, we introduce a matrix factorization technique to recover user preferences between rated items and unrated ones in the light of both user-user and item-item similarities. In addition, we claim that social trust relationships also have an important impact on a user’s preference for a specific item. Experimental results on three real-world data sets demonstrate that our approach achieves superior ranking performance to other counterparts.

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

An exploration of improving prediction accuracy by constructing a multi-type clustering based recommendation framework

Existing clustering-based recommendation methods generally focus on the clustering of users, items or social trust relationships. Although demonstrated to be efficient and scalable to large-scale datasets, these methods are sensitive to the quality of clustering and still suffer from the problem of low accuracy. In order to solve this issue, in this paper, we propose a multi-type clustering based recommendation framework which systematically considers the trust-based user clustering, similarity-based user clustering and similarity-based item clustering to further improve the recommendation accuracy. A SVD (Singular Value Decomposition) signs-based community mining method is utilized to process the trust and distrust matrix in order to discover the trust-based user clusters. The PLSA (Probabilistic Latent Semantic Analysis)-based model is employed to explore the similarity-based user and item clusters. Then a clustering-based trust regularization term is proposed to incorporate the trust-based user clusters into the matrix factorization model. Comparative experiments on two real-world datasets demonstrate that our approach can better address the issues of data sparsity and cold start, and outperforms other state-of-the-art methods in terms of RMSE and MAE.

A novel recommendation approach based on users’ weighted trust relations and the rating similarities

With the growing popularity of open social networks, approaches incorporating social relationships into recommender systems are gaining momentum, especially matrix factorization-based ones. The experiments in previous literatures indicate that social information is very effective in improving the performance of traditional recommendation algorithms. However, most of existing social recommendation methods only take one kind of social relations--trust information into consideration, which is far from satisfactory. Furthermore, most of the existing trust networks are binary, which results in the equal treatment to different users who are trusted by the same user in these methods. In this paper, based on matrix factorization methods, we propose a new approach to make recommendation with social information. Its novelty can be summarized as follows: (1) it shows how to add different weights on the social trust relationships among users based on the trustee's competence and trustworthiness; (2) it incorporates the similarity relationships among users as a complement into the social trust relationships to enhance the computation of user's neighborhood; (3) it can balance the influence of these two kinds of relationships based on user's individuality adaptively. Experiments on Epinions and Ciao datasets demonstrate that our approach outperforms the state-of-the-art algorithms in terms of mean absolute error and root mean square error, in particular for the users who rated a few items.

Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems

Although demonstrated to be efficient and scalable to large-scale data sets, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we develop a multiview clustering method through which users are iteratively clustered from the views of both rating patterns and social trust relationships. To accommodate users who appear in two different clusters simultaneously, we employ a support vector regression model to determine a prediction for a given item, based on user-, item- and prediction-related features. To accommodate (cold) users who cannot be clustered due to insufficient data, we propose a probabilistic method to derive a prediction from the views of both ratings and trust relationships. Experimental results on three real-world data sets demonstrate that our approach can effectively improve both the accuracy and coverage of recommendations as well as in the cold start situation, moving clustering-based recommender systems closer towards practical use.

Temporal dynamics in social trust prediction

Inferring unknown social trust relations attracts increasing attention in recent years. However, social trust, as a social concept, is intrinsically dynamic, and exploiting temporal dynamics provides challenges and opportunities for social trust prediction. In this paper, we investigate social trust prediction by exploiting temporal dynamics. In particular, we model the dynamics of user preferences in two principled ways. The first one focuses on temporal weight; the second one targets temporal smoothness. By incorporating these two types of temporal dynamics into traditional matrix factorization based social trust prediction model, two extended social trust prediction models are proposed and the corresponding algorithms to solve the models are designed too. We conduct experiments on a real-world dataset and the results demonstrate the effectiveness of our proposed new models. Further experiments are also conducted to understand the importance of temporal dynamics in social trust prediction.