2,102 research outputs found
Trust beyond reputation: A computational trust model based on stereotypes
Models of computational trust support users in taking decisions. They are
commonly used to guide users' judgements in online auction sites; or to
determine quality of contributions in Web 2.0 sites. However, most existing
systems require historical information about the past behavior of the specific
agent being judged. In contrast, in real life, to anticipate and to predict a
stranger's actions in absence of the knowledge of such behavioral history, we
often use our "instinct"- essentially stereotypes developed from our past
interactions with other "similar" persons. In this paper, we propose
StereoTrust, a computational trust model inspired by stereotypes as used in
real-life. A stereotype contains certain features of agents and an expected
outcome of the transaction. When facing a stranger, an agent derives its trust
by aggregating stereotypes matching the stranger's profile. Since stereotypes
are formed locally, recommendations stem from the trustor's own personal
experiences and perspective. Historical behavioral information, when available,
can be used to refine the analysis. According to our experiments using
Epinions.com dataset, StereoTrust compares favorably with existing trust models
that use different kinds of information and more complete historical
information
Collusion in Peer-to-Peer Systems
Peer-to-peer systems have reached a widespread use, ranging from academic and industrial applications to home entertainment. The key advantage of this paradigm lies in its scalability and flexibility, consequences of the participants sharing their resources for the common welfare. Security in such systems is a desirable goal. For example, when mission-critical operations or bank transactions are involved, their effectiveness strongly depends on the perception that users have about the system dependability and trustworthiness. A major threat to the security of these systems is the phenomenon of collusion. Peers can be selfish colluders, when they try to fool the system to gain unfair advantages over other peers, or malicious, when their purpose is to subvert the system or disturb other users. The problem, however, has received so far only a marginal attention by the research community. While several solutions exist to counter attacks in peer-to-peer systems, very few of them are meant to directly counter colluders and their attacks. Reputation, micro-payments, and concepts of game theory are currently used as the main means to obtain fairness in the usage of the resources. Our goal is to provide an overview of the topic by examining the key issues involved. We measure the relevance of the problem in the current literature and the effectiveness of existing philosophies against it, to suggest fruitful directions in the further development of the field
Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
In the context of Social TV, the increasing popularity of first and second
screen users, interacting and posting content online, illustrates new business
opportunities and related technical challenges, in order to enrich user
experience on such environments. SAM (Socializing Around Media) project uses
Social Media-connected infrastructure to deal with the aforementioned
challenges, providing intelligent user context management models and mechanisms
capturing social patterns, to apply collaborative filtering techniques and
personalized recommendations towards this direction. This paper presents the
Context Management mechanism of SAM, running in a Social TV environment to
provide smart recommendations for first and second screen content. Work
presented is evaluated using real movie rating dataset found online, to
validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging
Technologies for Education. SETE 201
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