The state-of-the-art in personalized recommender systems for social networking

With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0

Conditional Reasoning with Subjective Logic

Conditional inference plays a central role in logical and Bayesian reasoning, and is used in a wide range of applications. It basically consists of expressing conditional relationship between parent and child propositions, and then to combine those conditionals with evidence about the parent propositions in order to infer conclusions about the child propositions. While conditional reasoning is a well established part of classical binary logic and probability calculus, its extension to belief theory has only recently been proposed. Subjective opinions represent a special type of general belief functions. This article focuses on conditional reasoning in subjective logic where beliefs are represented in the form of binomial or multinomial subjective opinions. Binomial conditional reasoning operators for subjective logic have been defined in previous contributions. We extend this approach to multinomial opinions, thereby making it possible to represent conditional and evidence opinions on frames of arbitrary size. This makes subjective logic a powerful tool for conditional reasoning in situations involving ignorance and partial information, and makes it possible to analyse Bayesian network models with uncertain probabilities

A Logic For Uncertain Probabilities

We first describe a metric for uncertain probabilities called opinion, and subsequently a set of logical operators that can be used for logical reasoning with uncertain propositions. This framework which is called subjective logic uses elements from the Dempster-Shafer belief theory and we show that it is compatible with binary logic and probability calculus

Probabilistic Logic under Uncertainty

Probabilistic logic combines the capability of binary logic to express the structure of argument models with the capacity of probabilities to express degrees of truth of those arguments. The limitation of traditional probabilistic logic is that it is unable to express uncertainty about the probability values themselves. This paper provides a brief overview subjective logic which is a probabilistic logic that explicitly takes uncertainty about probability values into account. More specifically, we describe equivalent representations of uncertain probabilities, and their interpretations. Subjective logic is directly compatible with binary logic, probability calculus and classical probabilistic logic. The advantage of using subjective logic is that real world situations can be more realistically modelled, and that conclusions more correctly reflect the ignorance and uncertainties about the input arguments

Analysing the Relationship Between Risk and Trust

Among the various human factors impinging upon making a decision in an uncertain environment, risk and trust are surely crucial ones. Several models for trust have been proposed in the literature but few explicitly take risk into account. This paper analyses the relationship between the two concepts by first looking at how a decision is made based on the risk information. We then draw a model based on a decision surface, and finally derive from it a trust model, called the decision trust, in order to explicitly include risk into the trusting decision

Usability and Privacy in Identity Management Architectures

Digital identities represent who we are when engaging in online activities and transactions. The rapid growth in the number of online services leads to in an increasing number of different identities that each user needs to manage. As a result, many people feel overloaded with identities and suffer from password fatigue. This is a serious problem and makes people unable properly control and protect their digital identities against identity theft. This paper discusses the usability and privacy in online identity management solutions, and proposed a general approach for making users better able to control and manage their digital identities, as well as for creating more secure identity management solutions. More specifically, we propose a user-centric approach based on hardware and software technology on the user-side with the aim of assisting users when accessing online services

Trust and reputation management in web-based social network

know personally. They also communicate with other members of the network who are the friends of their friends and may be friends of their friend’s network. They share their experiences and opinions within the social network about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Opinions, reputations and ecommendations will influence users' choice and usage of online resources. Recommendations may be received through a chain of friends of friends, so the problem for the user is to be able to evaluate various types of trust recommendations and reputations. This opinion or ecommendation has a great influence to choose to use or enjoy the item by the other user of the community. Users share information on the level of trust they explicitly assign to other users. This trust can be used to determine while taking decision based on any recommendation. In case of the absence of direct connection of the recommender user, propagated trust could be useful