A Particle Swarm Approach to Collaborative Filtering based Recommender Systems through Fuzzy Features

AbstractCollaborative filtering (CF) either memory based or model based, has been emerged as an information filtering tool that provides effective recommendations to users utilizing the experiences and opinions of their similar neighbors when they interact with large information spaces. Memory based CF is more accurate than model based CF but it is less scalable. Our work in this paper is an attempt towards introducing a recommendation strategy (FPSO-CF) based on user hybrid features that retains the accuracy of memory – based CF as well as the scalability of model-based CF in an efficient manner. Since most user features are imprecise in nature, therefore these can be represented more naturally by using fuzzy sets. In this work, we employ particle swarm optimization algorithm (PSO) to learn user weights on various features and use fuzzy sets for representing user features efficiently. Effectiveness of our proposed RS (FPSO-CF) is demonstrated through experimental results in terms of various performance measures using the MovieLens dataset

Dynamical trust and reputation computation model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce

روشی نوین برای محاسبه اعتماد در شبکه‌های اجتماعی موبایلی

شبکه‌های اجتماعی موبایلی موجب تسهیل ارتباطات از طریق موبایل می‌شوند که کاربران این شبکه‌ها از موبایل به‌منظور دسترسی، اشتراک و توزیع اطلاعات استفاده می‌کنند. با افزایش روزافزون کاربران در شبکه‌های اجتماعی، حجم زیادی از اطلاعات به اشتراک گذاشته می‌شود که مشکلاتی ازجمله انتشار مطالب نادرست و شایعات دروغ را نیز به دنبال دارد. در این زمینه قوی‌ترین عامل برای سنجش صحت اطلاعات، استفاده از اعتبار هر کاربر به‌عنوان منبع توزیع اطلاعات است. اعتبار هر کاربر به‌عنوان منبع پخش اطلاعات می‌تواند بر اساس اعتماد دیگر کاربران به آن کاربر محاسبه شود. با توجه به ذهنی و ادراکی بودن مفهوم اعتماد، نگاشت اعتماد به یک مدل محاسباتی یکی از مسائل مهم در سیستم‌های محاسباتی شبکه‌های اجتماعی است. ازجمله پیچیدگی‌های فرآیند محاسبه اعتماد در این شبکه‌ها توجه به این موضوع است که در شبکه‌های اجتماعی، اجتماعات گوناگونی وجود داشته که همه کاربران آن‌ها به‌صورت مستقیم به یکدیگر متصل نمی‌باشند. در این مقاله با استفاده از ویژگی‌های کاربران در شبکه‌های اجتماعی، روشی منطبق بر منطق فازی برای دسته‌بندی کاربران پیشنهادشده است که اعتماد بین کاربران واقع در یک دسته با استفاده از مدل پیشنهادی محاسبه می‌شود. هم‌چنین با استفاده از فرآیندهای ترکیب، انتقال و اجتماع اعتمادها، اعتماد بین کاربرانی که به‌صورت مستقیم به یکدیگر متصل نیستند نیز بدست می‌آید. بررسی نتایج بیانگر این مسئله است که روش پیشنهادشده اعتماد افراد را در یک شبکه با دقت قابل قبولی معین می‌سازد

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Networks and trust: systems for understanding and supporting internet security

Includes bibliographical references.2022 Fall.This dissertation takes a systems-level view of the multitude of existing trust management systems to make sense of when, where and how (or, in some cases, if) each is best utilized. Trust is a belief by one person that by transacting with another person (or organization) within a specific context, a positive outcome will result. Trust serves as a heuristic that enables us to simplify the dozens decisions we make each day about whom we will transact with. In today's hyperconnected world, in which for many people a bulk of their daily transactions related to business, entertainment, news, and even critical services like healthcare take place online, we tend to rely even more on heuristics like trust to help us simplify complex decisions. Thus, trust plays a critical role in online transactions. For this reason, over the past several decades researchers have developed a plethora of trust metrics and trust management systems for use in online systems. These systems have been most frequently applied to improve recommender systems and reputation systems. They have been designed for and applied to varied online systems including peer-to-peer (P2P) filesharing networks, e-commerce platforms, online social networks, messaging and communication networks, sensor networks, distributed computing networks, and others. However, comparatively little research has examined the effects on individuals, organizations or society of the presence or absence of trust in online sociotechnical systems. Using these existing trust metrics and trust management systems, we design a set of experiments to benchmark the performance of these existing systems, which rely heavily on network analysis methods. Drawing on the experiments' results, we propose a heuristic decision-making framework for selecting a trust management system for use in online systems. In this dissertation we also investigate several related but distinct aspects of trust in online sociotechnical systems. Using network/graph analysis methods, we examine how trust (or lack of trust) affects the performance of online networks in terms of security and quality of service. We explore the structure and behavior of online networks including Twitter, GitHub, and Reddit through the lens of trust. We find that higher levels of trust within a network are associated with more spread of misinformation (a form of cybersecurity threat, according to the US CISA) on Twitter. We also find that higher levels of trust in open source developer networks on GitHub are associated with more frequent incidences of cybersecurity vulnerabilities. Using our experimental and empirical findings previously described, we apply the Systems Engineering Process to design and prototype a trust management tool for use on Reddit, which we dub Coni the Trust Moderating Bot. Coni is, to the best of our knowledge, the first trust management tool designed specifically for use on the Reddit platform. Through our work with Coni, we develop and present a blueprint for constructing a Reddit trust tool which not only measures trust levels, but can use these trust levels to take actions on Reddit to improve the quality of submissions within the community (a subreddit)