Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network ...

In this paper, we present a trust-based message propagation and evaluation framework in vehicular ad-hoc networks where peers share information regarding road condition or safety and others provide opinions about whether the information can be trusted. More specifically, our trust-based message propagation model collects and propagates peers’ opinions in an efficient, secure and scalable way by...

Introducing trust and reputation into multi-agent systems can significantly improve the quality and efficiency of the systems. The computational trust and reputation also creates an environment of survival of the fittest to help agents recognize and eliminate malevolent agents in the virtual society. The thesis redefines the computational trust and analyzes its features from different aspects. ...

Web spamming describes behavior that attempts to deceive search engine’s ranking algorithms. TrustRank is a recent algorithm that can combat web spam by propagating trust among web pages. However, TrustRank propagates trust among web pages based on the number of outgoing links, which is also how PageRank propagates authority scores among Web pages. This type of propagation may be suited for pro...

Recommender systems(RS) are a type of solution to the information overload problem suffered by users of websites that allow the rating of certain items. Collaborative filtering(CF) is one of the most widely used methods in personalized RS. The most critical part of collaborative filtering is to compute similarities among users using a user-item rating matrix based on which recommendations can b...

In the age of information explosion, Internet facilitates product searching and collecting much more convenient for users. However, it is time-consuming and exhausting for users to deal with large amounts of product information. In response, various recommendation approaches have been developed to recommend products that match users’ preferences and requirements. In addition to the well-known c...