A model of a trust-based recommendation system on a social network

In this paper, we present a model of a trust-based recommendation system on a social network. The idea of the model is that agents use their social network to reach information and their trust relationships to filter it. We investigate how the dynamics of trust among agents affect the performance of the system by comparing it to a frequency-based recommendation system. Furthermore, we identify the impact of network density, preference heterogeneity among agents, and knowledge sparseness to be crucial factors for the performance of the system. The system self-organises in a state with performance near to the optimum; the performance on the global level is an emergent property of the system, achieved without explicit coordination from the local interactions of agent

The KAA project: a trust policy point of view

In the context of ambient networks where each small device must trust its neighborhood rather than a fixed network, we propose in this paper a \textit{trust management framework} inspired by known social patterns and based on the following statements: each mobile constructs itself a local level of trust what means that it does not accept recommendation by other peers, and the only relevant parameter, beyond some special cases discussed later, to evaluate the level of trust is the number of common trusted mobiles. These trusted mobiles are considered as entries in a local database called history for each device and we use identity-based cryptography to ensure strong security: history must be a non-tansferable object

Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H-OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.16 page(s

Simulating social relations in multi-agent systems

Open distributed systems are comprised of a large number of heterogeneous nodes with disparate requirements and objectives, a number of which may not conform to the system specification. This thesis argues that activity in such systems can be regulated by using distributed mechanisms inspired by social science theories regarding similarity /kinship, trust, reputation, recommendation and economics. This makes it possible to create scalable and robust agent societies which can adapt to overcome structural impediments and provide inherent defence against malicious and incompetent action, without detriment to system functionality and performance. In particular this thesis describes: • an agent based simulation and animation platform (PreSage), which offers the agent developer and society designer a suite of powerful tools for creating, simulating and visualising agent societies from both a local and global perspective. • a social information dissemination system (SID) based on principles of self organisation which personalises recommendation and directs information dissemination. • a computational socio-cognitive and economic framework (CScEF) which integrates and extends socio-cognitive theories of trust, reputation and recommendation with basic economic theory. • results from two simulation studies investigating the performance of SID and the CScEF. The results show the production of a generic, reusable and scalable platform for developing and animating agent societies, and its contribution to the community as an open source tool. Secondly specific results, regarding the application of SID and CScEF, show that revealing outcomes of using socio-technical mechanisms to condition agent interactions can be demonstrated and identified by using Presage.Open Acces

Location-Based Social Networks: Latent Topics Mining and Hybrid Trust-Based Recommendation

The rapid advances of the 4th generation mobile networks, social media and the ubiquity of the advanced mobile devices in which GPS modules are embedded have enabled the location-based services, especially the Location-Based Social Networks (LBSNs) such as Foursquare and Facebook Places. LBSNs have been attracting more and more users by providing services that integrate social activities with geographic information. In LBSNs, a user can explore places of interests around his current location, check in at these venues and also selectively share his check-ins with the public or his friends. LBSNs have accumulated large amounts of information related to personal or social activities along with their associated location information. Analyzing and mining LBSN information are important to understand human preferences related to locations and their mobility patterns. Therefore, in this thesis, we aim to understand the human mobility behavior and patterns based on huge amounts of information available on LBSNs and provide a hybrid trust-based POI recommendation for LBSN users. In this dissertation, we first carry out a comprehensive and quantitative analysis about venue popularity based on a cumulative dataset collected from greater Pittsburgh area in Foursquare. It provides a general understanding of the online population's preferences on locations. Then, we employ a probabilistic graphical model to mine the check-in dataset to discover the local geographic topics that capture the potential and intrinsic relations among the locations in accordance with users' check-in histories. We also investigate the local geographic topics with different temporal aspects. Moreover, we explore the geographic topics based on travelers' check-ins. The proposed approach for mining the latent geographic topics successfully addresses the challenges of understanding location preferences of groups of users. Lastly, we focus on individual user's preferences of locations and propose a hybrid trust-based POI recommendation algorithm in this thesis. The proposed approach integrates the trust based on both users' social relationship and users' check-in behavior to provide POI recommendations. We implement the proposed hybrid trust-based recommendation algorithm and evaluate it based on the Foursquare dataset and the experimental results show good performances of our proposed algorithm

Building Web Based Collaboration Systems in Supply Chain Management: A Conceptual Framework

Good collaboration plays an important role in effective supply chain management. The main obstacle to collaboration within a supply chain is a conflict between each enterprise’s local optimization and the chain’s global optimization. We show that in addition to relationships, trust, contract and other social factors, sharing and reallocation of payoff are critical to align the objective of each member enterprise from local optimization to global optimization. We propose collaboration systems, which take advantage of information technology in the digital economy, to set up payoff reallocation and information sharing mechanism. These systems can be used to foster solid collaboration relationships within one supply chain. We identify the system requirements and outline the Web-based systems schema. The collaboration systems have four indivisible components: measuring performance; monitoring performance and payoff re-allocation; global optimization algorithm, and reconfiguration: planning, forecasting and recommendation. The challenges, impediments and enablers to implement the proposed collaboration systems are also discussed in the paper

Trust Management in Social Internet of Things (SIoT): A Survey

A survey on trust management in the Social Internet of Things (SIoT) is provided, beginning with a discussion of SIoT architectures and relationships. Using a variety of publication databases, we describe efforts that focus on various trust management aspects of SIoT. Trust management models comprise three themes: trust computation, aggregation, and updates. Our study presents a detailed discussion of all three steps. Trust computation and trust aggregation depend upon Trust Attributes (TAs) for the calculation of local and global trust values. Our paper discusses many strategies for aggregating trust, but “Weighted Sum” is the most frequently used in the relevant studies. Our paper addresses trust computation and aggregation scenarios. Our work classifies research by TAs (Social Trust, Quality of Service). We’ve categorized the research (reputation-based, recommendation-based, knowledge-based) depending on the types of feedback/opinions used to calculate trust values (global feedback/opinion, feedback from a friend, trustor’s own opinion considering the trustee’s information). Our work classifies studies (policy-based, prediction-based, weighted sum-based/weighted linear combination-based) by trust computation/aggregation approach. Two trust-update schemes are discussed: time-driven and event-driven schemes, while most trust management models utilize an event-driven scheme. Both trust computation and aggregation need propagating trust values in a centralized, decentralized, or semi-centralized way. Our study covers classifying research by trust updates and propagation techniques. Trust models should provide resiliency to SIoT attacks. This analysis classifies SIoT attacks as collaborative or individual. We also discuss scenarios depicted in the relevant studies to incorporate resistance against trust-related attacks in SIoT. Studies suggest context-based or context-free trust management strategies. Our study categorizes studies based on context-based or context-free approaches. To gain the benefits of an immutable, privacy-preserving approach, a future trust management system should utilize Blockchain technology to support non-repudiation and tracking of trust relationships

Hybrid recommender systems for personalized government-to-business e-services

University of Technology, Sydney. Faculty of Engineering and Information Technology.As e-Governments around the world face growing pressures to improve the quality of service delivery and become more efficient and cost-effective, their initiatives currently focus on providing users with a seamless service delivery experience. Webbased technologies offer governments more efficient and effective means than traditional physical channels to provide high quality e-Service delivery to their users, which include citizens and businesses. Government-to-Business (G2B) e-Services involve information distribution, transactions, and interactions with businesses m varying ways via e-Government websites and portals. The G2B e-Services aim to reduce burdens on businesses and to provide effective and efficient access to information for business users. One of the most important e-Services of G2B is the promotion of local businesses goods and services to consumers (i.e., local and overseas businesses) by providing on line business directories. However, with the rapid growth of information and unreliable search facilities, busine s users, who are seeking 'one-to-one' e-Services from government in highly competitive markets, struggle with online business directories and increasingly find it difficult to locate business pa1tners according to their needs and interests. How, then, can business users be provided with inforn1ation and services specific to their needs, rather than an undifferentiated mass of information? An effective solution proposed in this research is the development of personalized G2B e-Services using recommender systems. It is worth mentioning that the adoption of recommender systems in the context of e- Government to provide personalized services has received very limited attention in the literature. Recommender systems aim to suggest the right items (products, services or information) that best match the needs and interests of particular users based on their explicit and implicit preferences. In current recommender systems, the Collaborative Filtering (CF) approaches are the most popular and widely adopted recommendation approaches. Regardless of the success of CF-based approaches in various recommendation applications, they still suffer from data uncertainty, data sparsity, cold-start item and cold-start user problems, resulting in poor recommendation accuracy and reduced coverage. An effective solution proposed in this research to alleviate such problems is the development of hybrid and fusion-based recommendation algorithms that exploit and incorporate additional knowledge about users and items. Such knowledge can be extracted from either the users ' trust social network or the items' semantic domain knowledge. This research explores the adoption of recommender systems m an e- Govemment context for the provision of personalized G2B e-Services. Accordingly, a G2B recommendation framework for providing personalized G2B e-Services (particularly personalized business partner recommendations) for Small-to-Medium Businesses (SMBs) is proposed. Novel hybrid and fusion-based recommendation models and algorithms are also proposed and developed to overcome the limitations of existing CF-based recommendation approaches. Experimental results on real datasets show that our proposed recommendation algorithms significantly outperfmm existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with data sparsity, cold-start item and cold-start user limitations inherent in CF-based recommendation approaches

Culture and disaster risk management - stakeholder attitudes during Stakeholder Assembly in Lisbon, Portugal

This report provides a summary of the topics discussed and the results of the third CARISMAND Stakeholder Assembly conducted in Lisbon, Portugal on 27-28 February 2018. In order to promote cross-sectional knowledge transfer and gather a variety of attitudes and perceptions, as in the first and second CARISMAND Stakeholder Assemblies held in Romania and Italy in the previous years, the audience consisted of a wide range of practitioners who are typically involved in disaster management, e.g., civil protection, the emergency services, paramedics, nurses, environmental protection, Red Cross, firefighters, military, and the police. Further, these practitioners were from several regions in Portugal, including the island of Madeira. The 40 participants were recruited via invitations sent to various Portuguese organisations and institutions, and via direct contacts of the Civil Protection Department in Lisbon which is one of the partners in the CARISMAND consortium. The event consisted of a mix of presentations and discussion groups to combine dissemination with information gathering (for the detailed schedule/programme see Appendix 1). Furthermore, this third Stakeholder Assembly was organised and specifically designed to discuss and collect feedback on a comprehensive set of recommendations for disaster practitioners, which will form one of the core elements of the CARISMAND Work Package 9 ‘Toolkit’. These recommendations, which have all been formulated on the basis of Work Packages 2-10 results, were structured in four, main “sets”: 1. Approaches to ethnicity in disaster management; 2. Culturally aware disaster-related training activities; 3. Cultural factors in disaster communication, with the sub-sets: a. Cultural values and emotions; (cross-)cultural symbols; “physical” aides and methods; b. Involvement of cultural leaders; involvement of specific groups; usage of social media and mobile phone apps; and 4. Improving trust, improving disaster management. In an initial general assembly, the event started with presentations of the CARISMAND project and its main goals and concepts, including the concept of culture adopted by CARISMAND, and the planned CARISMAND Toolkit architecture and functionalities. These were followed by a detailed presentation of the first of the above mentioned sets of recommendations for practitioners. Then, participants of the Stakeholder Assembly were split into small groups in separate breakout rooms, where they discussed and provided feedback to the presented recommendations. Over the course of the 2-day event, this procedure was followed for all four sets of recommendations. To follow the cyclical design of CARISMAND events, and wherever meaningful and possible, the respective Toolkit recommendations for practitioners provided also the basis for a respective “shadow” recommendation for citizens which will be discussed accordingly in the last round of CARISMAND Citizen Summits (Citizen Summit 5 in Lisbon, and Citizen Summit 6 in Utrecht) in 2018. The location of the Third Stakeholder Assembly was selected to make use of the extensive local professional network of the Civil Protection Department in Lisbon, but also due to Portugal being a traditional “melting pot” where, over more than a millennium, people from different cultural backgrounds and local/ethnical origins (in particular Africa, South America, and Europe) have lived both alongside and together. All documents related to the Working Groups, i.e. discussion guidelines and consent forms, were translated into Portuguese. Accordingly, all presentations, as well as the group discussions were held in Portuguese, aiming to avoid any language/education-related access restrictions, and allowing participating practitioners to respond intuitively and discuss freely in their native language. For this purpose, simultaneous interpreters and professional local moderators were contracted via a local market research agency (EquaçãoLógica), which also provided the basic data analysis of all Working Group discussions and an independent qualitative evaluation of all recommendations presented in the event. The results of this analysis and evaluation will demonstrate that most recommendations were seen by the participating practitioners to be relevant and useful. In particular, those recommendations related to the use of cultural symbols and the potential of mobile phone apps and/or social media were perceived as stimulating and thought-provoking. Some recommendations were felt to be less relevant in the specific Portuguese context, but accepted as useful in other locations; a very small number was perceived to be better addressed to policy makers rather than practitioners. These and all other suggestions for improvement of the presented CARISMAND Toolkit recommendations for practitioners have been taken up and will be outlined in the final chapter of this report.The project was co-funded by the European Commission within the Horizon2020 Programme (2014-2020).peer-reviewe