Living in the information-overloading age, many people find it difficult to assimilate the information and to identify resources they need. As to a consumer, browsing, searching, and buying a product on online stores is often a time-consuming and frustrating task with the flourishing development of e-commerce. Many shoppers who are interested in buying products on E-commerce websites end up finding nothing they want. Therefore, many E-commerce websites have implemented recommender systems that intend to provide consumers with personalized recommendations for various types of products and services. Some recent research has taken into account social influence in recommender systems in E-commerce. These recommender systems have been observed to achieve better accuracy of prediction, and have also overcome some of the problems of the previous methods. In this study, we propose a trust network-based recommendation framework that utilizes the trust relationship between users to generate recommendation. We employ PageRank algorithm for trust matrix adjustment and recommendation. In addition, we propose several assumptions that can be used to construct trust matrix, and we verify them by experiments. We finally identify two approaches for adjusting trust matrix. Bases on the trust and rating data collected from Epinion.com, we exercise several alternatives and evaluated many combinations of trust matrix adjustment and recommendation methods. Our experiment evaluation results show that using different pagerank for different users groups can generate better recommendation results. Moreover, we proposed a best hybrid method that achieves the best performance.

A social network represents the interconnected relations among people. In a knowledge-intensive era as of now, people have less capability to resolve more ill-defined and complicated problems. Several researches indicate that under such a circumstance people are more likely to turn to other people through their social networks than to consult sources like databases and documents. Searching in social networks is therefore an essential issue. In addition, typical social networks are neither regular nor completely random ones, but instead, they are mixtures between these two, which are referred to as small worlds. Consequently, such an issue is also called the small world search. Search mechanism in the small world can be classified into single-attribute approach (e.g. best connected) and multiple-attribute approach (e.g. social distance). Relevant research works, however, are mostly based on acquaintance networks. And one of the problems to search in acquaintance networks is its high attrition rate that hinders further search and results in low success rate. On the other hand, in recent years several researchers focus on the constitution and propagation of trust networks that represent the trustworthy relations among people. Since trust implies much closer to what we mean friends rather than acquaintance, we believe that the attrition rate in trust networks should be lower than in acquaintance networks. Based on this belief, we propose to search in trust networks rather than acquaintance networks to enhance the quality of the search process. We design three experiments to compare the search performance in the trust networks and in the acquaintance networks. Experiment I is to examine the ¡§social-distance¡¨ search strategy we adopt in the search. The second experiment evaluates the performance comparison without considering attrition. Finally, we consider the attrition rate and attrition rate difference for the comparison. The results show that as long as the attrition rate difference is beyond 10%, search in trust networks performs better than in acquaintance networks. It therefore justifies the feasibility of our proposed approach in gaining good search performance.

Living in the information-overloading age, it is difficult to find the right information and identify the resources they need on the websites. As to a user, it is time-consuming in browsing, searching, and making a decision to buy products on online stores. Therefore, many E-commerce websites have implemented recommender systems that intend to provide users with professional recommendation for various types of products and services. Although many recommendation methods have been proposed, there are still some problems like the sparsity and the cold start problems. In addition, some researchers observe there exist users who are biased and products that are controversial. We conjecture that ratings given by biased users or given to controversial products may have impact on estimation accuracy of recommendation. In this thesis, we will examine the measures for user bias and product controversy and propose trust-based-recommendation techniques that take them into account. We evaluate the proposed techniques using the web of trust and rating data collected from the Epinions.com website. It is found that properly setting some parameters, the proposed trust network-based method that incorporates user bias achieve higher recommendation accuracy.

Recently, organization downsizing has become the major means used by corporations to seek survival or better growth. Organization downsizing has some purposes: to reduce the cost of personal, to get better efficiency, to rearrange the deployment of human resource after M&A. Besides those economic purposes, some scholar think corporations do organization downsizing to get legitimacy. Generally speaking, the purpose of organization downsizing is to get better efficiency or the legitimacy. But lots of researches indicate lots of organization downsizing can not achieve expected goals. Although some corporations can achieve the goal of organization downsizing, many corporations can¡¦t achieve expected goals, and there are also some corporations do a lot of organization downsizing but their situations go from bed to worse. Among the researches of why organization downsizing can¡¦t achieve expected goals, many researches indicate that the application of organization downsizing will make huge negative impact to survivors. Some scholars call that impact survivor syndrome. Besides, some scholars investigate the reason of the failure of organization downsizing from the point of informal social network. Because the theory of social capital includes trust, organization involvement, social network and so on, we can more understand the reason of the failure of organization downsizing from the point of social capital. So the purpose of this research is to use the theory of social capital to investigate the impact of organization downsizing and provide some advices to corporations, so that they can do better about organization downsizing. This research uses case study to understand the reason and the process of organization downsizing, and investigates the negative impact of organization downsizing. Survivor syndrome and social capital play important roles in the analysis of the failure of organization downsizing. This research finds that organization downsizing will do huge damage to social capital. If corporation don¡¦t understand the importance and benefit of social network, then the application of organization downsizing will hurt social network and corporations can¡¦t achieve expected goals. Besides, in the analysis of case study this research finds that organization downsizing also hurt trust, involvement, incentives to cooperation and so on, these issues are part of survivor syndrome, but we also can use social capital to explain.

Made available in DSpace on 2015-03-05T13:59:44Z (GMT). No. of bitstreams: 0 Previous issue date: 28 / Nenhuma / Este trabalho apresenta Flexibel Secure Service Discovery (FSSD), um protocolo para a descoberta de serviços em sistemas ubíquos. Seu projeto é centrado no compromisso entre os níveis de colaboração, segurança e privacidade que os participantes desejam na descoberta. A abordagem proposta oferece gerenciamento de confiança, além de mecanismos de controle de exposição e de acesso descentralizados. As propriedades do protocolo foram avaliadas através de simulações, variando-se os níveis de segurança e privacidade do sistema para demonstrar que a abordagem proposta lida adequadamente com o compromisso em relação à colaboração entre pares / This work presents Flexibel Secure Service Discovery (FSSD), a protocol for service discovery in ubiquitous systems. Its design is centered by the participants. The proposed approach provides trust management, in addition to descentralized mechanisms to control the exposure and access to the service information. The protocol properties were evaluated with simulation, by varying both security and privacy levels of the system in order to demonstrate that the proposed approach properly addresses the tradeoff regarding peer collaboration

Today, there are user annotated internet sites, user interaction logs, online user communities which are valuable sources of information concerning the personalized recommendation problem. In the literature, hybrid social recommender systems have been proposed to reduce the sparsity of the usage data by integrating the user related information sources together. In this thesis, a method based on probabilistic latent semantic analysis is used as a framework for a hybrid social recommendation system. Different data hybridization approaches on probabilistic latent semantic analysis are experimented. Based on this flexible probabilistic model, network regularization and model blending approaches are applied on probabilistic latent semantic analysis model as a solution for social trust network usage throughout the collaborative filtering process. The proposed model has outperformed the baseline methods in our experiments. As a result of the research, it is shown that the proposed methods successfully model the rating and social trust data together in a theoretically principled way.

Growing an online community takes time and effort. Relationships in an online community must be initiated based on trust followed by privacy, and then carefully cultivated. People are using web based social networks more than recent past, but they always want to protect their private data from unknown access; meanwhile also eager to know more people whom they are interested. Among all other system, trust based recommenders have been one of the most used and demanding system which takes the advantage of social trust to generate more accurate predictions. In this work we have proposed for Meven (An Enterprise trust-based profile recommendation with privacy), which uses Social Network Content (User Profiles and trends) with Trust and privacy control policy. The idea of system is to provide Social Networks with the ability to quickly find related information about the users having similar behaviors as the current user. The users will also be able to set the privacy metrics on their profiles so they will not get recommendation of those they feel less important and this is achieved by Privacy metrics. To generate accurate predictions, we defined trust between two users as a strong bond which is computed using different metrics based on user’s activities with respect to different content such as blogging, writing articles, commenting, and liking along with profile information such as organization, region, interests or skills. We have also introduced privacy metric in such a way so that users have full freedom to hide themselves from the recommendation system or they can also have the opportunity to customize their profiles to be visible to certain level of trustworthy users. We have exposed our application as a web service(api) so that any social network web portal can access the recommendations and publish them as a widget in social network.