Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, bu...

This paper explores the contingent role that social ties play in the emergence of status hierarchies. We argue that, while status is formed based on actors’ perception and understanding of social cues, network structure and position influence this process by influencing the attention and legitimacy given to the focal actor in accordance with social cues that signal an actor’s identity. Using a ...

Since the emergence of the Internet era in the early 1990s, many evaluation systems for the rating of various entities emerged. For example, at amazon.com people are able to rate books and transactions or to leave comments for other users. Although there is a wide variety of rating and evaluation mechanisms on the Internet, there are only few mechanisms for the evaluation of user skills in soci...

The data used is MovieLens 1M from http://www.grouplens.org/node/73 which consists of 1 million ratings from 6000 users on 4000 movies. Due to huge number of nodes and edges, I only sample a small portion of the entire dataset which includes 11398 ratings from 86 users on 2154 movies during a week in 2000. Each user rates a movie using integer score ranging from 1 to 5 where larger score indica...

social learning and knowledge generation are two key factors to achieve successful natural resources co-management. social learning can be beneficial by broadcasting and transmitting experiences and problem solving ideas in a group of people. social learning networks can transmit knowledge and experiences from one node to another in the network. social learning can produce a suitable learning a...

Recommendation System (RS) is the tool, which helps to find interesting and relevant items or products. With the dawn of social network and its attractiveness, people are interested to share their experience, such as rating, reviews, etc. which helps to recommend the items of user interest. The potential growth of the internet results the use of social networks such as Facebook, Twitter, linked...

We consider a departure from net neutrality by an Internet service provider (ISP) that financially discriminates among content providers through exclusive zero-rating contracts. Zero-rating is instrument to distort competition between and the manner in which consumers value content. analyze its implications for incentives provide quality market invest broadband infrastructure. makes more expens...

In this project, we apply two predictive network algorithms to the Yelp academic challenge dataset. The first algorithm is adapted from Leskovec, Huttenlocher and Kleinberg (2010) to predict the rating that a user will assign to a business [1]. We adapt the notion of signed edges in the network to account for both useruser edges (friendships) as well as user-business edges (reviews). A logistic...