PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information

Intergenerational entrepreneurship to foster sustainable development: A methodological training proposal

Intergenerational entrepreneurial initiatives are aimed at addressing the needs and opportunities of certain social groups and have the potential of becoming successful business projects. Moreover, they are a key to undertake sustainability practices that may represent a competitive advantage for the companies and an example to imitate when creating businesses. The objective of the study is to propose an intergenerational training methodology so that young people and seniors can create companies together, generating social cohesion and sustainable development in response to generational challenges. Intergenerational entrepreneurship seems to be a novel research area, especially when referring to developing methodologies of collaborative entrepreneurship projects. For this purpose, our literature review focuses on, first, the matching theories and experiences applied for intergenerational cooperation; then, literature about training methodologies for entrepreneurship is reviewed; finally, the main theories on training skills for entrepreneurship are approached. Focus groups were conducted as they serve as the main sources of data and are very appropriate for the generation of new ideas within a social context. In general, results show that, for achieving a successful intergenerational cooperation, some specific training is needed for both generations. This paper is a starting point for future research approaching intergenerational entrepreneurship, or entrepreneurial initiatives with singular characteristics, such as rural contexts or people with disabilitiesThis research was funded by IVI project (2018-1-FR01-KA204-047946). Erasmus+ Key Action 204, funded with support from the European Commissio

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

Improving matching process in social network

Online dating networks, a type of social network, are gaining popularity. With many people joining and being available in the network, users are overwhelmed with choices when choosing their ideal partners. This problem can be overcome by utilizing recommendation methods. However, traditional recommendation methods are ineffective and inefficient for online dating networks where the dataset is sparse and/or large and two-way matching is required. We propose a methodology by using clustering, SimRank to recommend matching candidates to users in an online dating network. Data from a live online dating network is used in evaluation. The success rate of recommendation obtained using the proposed method is compared with baseline success rate of the network and the performance is improved by double

Improving matching process in social network using implicit and explicit user information

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid social matching system that takes advantage of both collaborative and content-based concepts of recommendation. The clustering technique is used to reduce the number of users that the matching system needs to consider and to overcome other problems from which social matching systems suffer, such as cold start problem due to the absence of implicit information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased, using both user information (explicit data) and user behavior (implicit data)