Evaluating online trust using machine learning methods

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation model to evaluate recommenders\u27 trustworthiness and expertise; and constructs a distributed trust system and a global reputation model to achieve efficient trust computing and management. The experimental results show that the proposed three trust models are reliable. The contributions lie in: (1) novel application of neural networks in recommendation trust evaluation and distributed trust management; (2) adaptivity of the proposed neural network-based trust models to accommodate dynamic and multifacet properties of trust; (3) robustness of the neural network-based trust models to the noise in training data, such as deceptive recommendations; (4) efficiency and parallelism of computation and load balance in distributed trust evaluations; and (5) novel application of Hidden Markov Models in recommenders\u27 reputation evaluation

Study on Directed Trust Graph Based Recommendation for E-commerce System

Automated recommender systems have played a more and more important role in marketing and ever increasingly booming e-commerce systems. They provide useful predictions personalized recommendations according to customers’ characteristics and a variety of large and complex product offerings. In many of these recommendation technologies Collaborative Filtering (CF) has proven to be one of the most successful recommendation method, which has been widely used in many e-commerce systems. The success of CF recommendation depends mainly on locating similar neighbors to get recommendation items. However, many scholars have found that the process of finding similar neighbors often fail, due to some inherent weaknesses of CF based recommendation. In view of this, we propose a trust feedback recommendation algorithm based on directed trust graph (DTG), which is able to propagate trust relationship. In our approach, there is no need to compute similarity between users, but utilize the trust relation between them to conduct prediction calculation. Based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. Experimental evaluation on real life Epinions datasets shows that the effectiveness and practicability of our approach

How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance

© 2020 Elsevier Ltd Although algorithms have been widely used to deliver useful applications and services, it is unclear how users actually experience and interact with algorithm-driven services. This ambiguity is even more troubling in news recommendation algorithms, where thorny issues are complicated. This study investigates the user experience and usability of algorithms by focusing on users\u27 cognitive process to understand how qualities/features are received and transformed into experiences and interaction. This work examines how users perceive and feel about issues in news recommendations and how they interact and engage with algorithm-recommended news. It proposes an algorithm experience model of news recommendation integrating the heuristic process of cognitive, affective, and behavioral factors. The underlying algorithm can affect in different ways the user\u27s perception and trust of the system. The heuristic affect occurs when users\u27 subjective feelings about transparency and accuracy act as a mental shortcut: users considered transparent and accurate systems convenient and useful. The mediating role of trust suggests that establishing algorithmic trust between users and NRS could enhance algorithm performance. The model illustrates the users\u27 cognitive processes of perceptual judgment as well as the motivation behind user behaviors. The results highlight a link between news recommendation systems and user interaction, providing a clearer conceptualization of user-centered development and the evaluation of algorithm-based services

An Implementation of Role-Base Trust Management Extended with Weights on Mobile Devices

AbstractThis paper describes the implementation of a library for the management and evaluation of Role-based Trust Management (RT) credentials and policies written in RTML, also extended with weights, in mobile devices. In particular, it describes the implementation of the library in J2ME. It is worth noticing, that RTML credentials are XML-like documents and thus the capability of porting these features on mobile devices makes the overall framework very interoperable with other RT frameworks (as for GRID systems). As policy language, we use actually a variant of RTML, whose policies are added with weights and are able to express quantitative experience-based notions of trust. It allow also to encode certain reputation and recommendation models. The obtained results show how the implementation on mobile devices is feasible and the running time acceptable for several applications

Trust Management Model for Cloud Computing Environment

Software as a service or (SaaS) is a new software development and deployment paradigm over the cloud and offers Information Technology services dynamically as "on-demand" basis over the internet. Trust is one of the fundamental security concepts on storing and delivering such services. In general, trust factors are integrated into such existent security frameworks in order to add a security level to entities collaborations through the trust relationship. However, deploying trust factor in the secured cloud environment are more complex engineering task due to the existence of heterogeneous types of service providers and consumers. In this paper, a formal trust management model has been introduced to manage the trust and its properties for SaaS in cloud computing environment. The model is capable to represent the direct trust, recommended trust, reputation etc. formally. For the analysis of the trust properties in the cloud environment, the proposed approach estimates the trust value and uncertainty of each peer by computing decay function, number of positive interactions, reputation factor and satisfaction level for the collected information.Comment: 5 Pages, 2 Figures, Conferenc

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

Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest items to users that might be interesting for them. Recent studies illustrate that incorporating social trust in Matrix Factorization methods demonstrably improves accuracy of rating prediction. Such approaches mainly use the trust scores explicitly expressed by users. However, it is often challenging to have users provide explicit trust scores of each other. There exist quite a few works, which propose Trust Metrics to compute and predict trust scores between users based on their interactions. In this paper, first we present how social relation can be extracted from users' ratings to items by describing Hellinger distance between users in recommender systems. Then, we propose to incorporate the predicted trust scores into social matrix factorization models. By analyzing social relation extraction from three well-known real-world datasets, which both: trust and recommendation data available, we conclude that using the implicit social relation in social recommendation techniques has almost the same performance compared to the actual trust scores explicitly expressed by users. Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user. To the best of our knowledge, this is the first work to extend TrustSVD with extracted social trust information. The experimental results support the idea of employing implicit trust into matrix factorization whenever explicit trust is not available, can perform much better than the state-of-the-art approaches in user rating prediction