Mozhgan Tavakolifard

Social computing: an intersection of recommender systems, trust/reputation systems, and social networks

Computational applications now go beyond personal computing, facilitating collaboration and social interactions. Social computing is an area of information technology concerned with the intersection of human and social studies connected by computer networks. The primary goal of this article is to provide a brief survey of three popular social computing services: recommender systems, trust/reputation systems, and social networks. We approach these services from a data representation perspective and discuss two of their main challenges: network sparsity and coldstart problems. We also present a novel graph model, which provides an abstract taxonomy and a common data representation model for the three services. We are mainly motivated by the power of graph theory in data representation and analysis for social computing services. Through this model, we believe that it becomes clearer that data from different contexts can be related such that new solutions can be explored; thus, it may provide illumination for the aforementioned problems and stimulate new research.

Trust transferability among similar contexts

Trust is a fundamental concern in electronic transactions and behavior of people are influenced by the situation. Motivated by that we present a state-of-the-art survey of context representation in trust management and provide main directions along which research efforts have been done. We propose a generalized model which considers different aspects of the relationship between context-awareness and trust management.

Tailored news in the palm of your hand: a multi-perspective transparent approach to news recommendation

Mobile news recommender systems help users retrieve news that is relevant in their particular context and can be presented in ways that require minimal user interaction. In spite of the availability of contextual information about mobile users, though, current mobile news applications employ rather simple strategies for news recommendation. Our multi-perspective approach unifies temporal, locational, and preferential information to provide a more fine-grained recommendation strategy. This demo paper presents the implementation of our solution to efficiently recommend specific news articles from a large corpus of newly-published press releases in a way that closely matches a readers reading preferences.

Analogical Trust Reasoning

Trust is situation-specific and the trust judgment problem with which the truster is confronted might be, in some ways, similar but not identical to some problems the truster has previously encountered. The truster then may draw information from these past experiences useful for the current situation. We present a knowledge-intensive and model-based case-based reasoning framework that supports the truster to infer such information. The suggested method augments the typically sparse trust information by inferring the missing information from other situational conditions, and can better support situation-aware trust management. Our framework can be coupled with existing trust management models to make them situation-aware. It uses the underlying model of trust management to transfer trust information between situations. We validate the proposed framework for Subjective Logic trust management model and evaluate it by conducting experiments on a large real dataset.

A Taxonomy to Express Open Challenges in Trust and Reputation Systems

During the past decade, online trust and reputation systems have provided cogent answers to emerging challenges in the global computing infrastructures relating to computer and network security, electronic commerce, virtual enterprises, social networks and cloud computing. The goal of these systems in such global computing infrastructures is to allow entities to reason about the trustworthiness of other entities and to make autonomous decisions on the basis of trust. This requires the development of computational trust models that enable entities to reason about trust and to verify the properties of a particular interaction. The robustness of these mechanisms is one of the critical factors required for the success of this technology. In this paper, we briefly present characteristics of existing online trust and reputation models and systems through a multidimensional framework that can serve as a basis to understand the current state of the art in the area. The critical open challenges that limit the effectiveness of todays trust and reputation systems are discussed by providing a comprehensive literature review. Furthermore, we present a set of our contributions as a way to address some of these challenges.

A Probabilistic Reputation Algorithm for Decentralized Multi-Agent Environments

The importance of trust in electronic transactions is well understood. The majority of current trust models consist of a central entity that verifies compliance with the trust requirements, using standardized evaluation methods and criteria. In decentralized environments, the communication scenarios are more complex, and no universally accepted objective requirements or evaluation criteria exist. It should be noted that the situation would get even more complicated when agents are interacting with each other. The goal of this research is to model trust and reputation in decentralized multi-agent systems. To achieve this, we have chosen the Ntropi model, among several other models, as a starting point, The efficiency of the model in such scenarios has been significantly improved by introducing a new probabilistic reputation algorithm for the Ntropi model.

Inferring Trust Based on Similarity with TILLIT

A network of people having established trust relations and a model for propagation of related trust scores are fundamental building blocks in many of today’s most successful e-commerce and recommendation systems. However, the web of trust is often too sparse to predict trust values between non-familiar people with high accuracy. Trust inferences are transitive associations among users in the context of an underlying social network and may provide additional information to alleviate the consequences of the sparsity and possible cold-start problems. Such approaches are helpful, provided that a complete trust path exists between the two users. An alternative approach to the problem is advocated in this paper. Based on collaborative filtering one can exploit the like-mindedness resp. similarity of individuals to infer trust to yet unknown parties which increases the trust relations in the web. For instance, if one knows that with respect to a specific property, two parties are trusted alike by a large number of different trusters, one can assume that they are similar. Thus, if one has a certain degree of trust to the one party, one can safely assume a very similar trustworthiness of the other one. In an attempt to provide high quality recommendations and proper initial trust values even when no complete trust propagation path or user profile exists, we propose TILLIT — a model based on combination of trust inferences and user similarity. The similarity is derived from the structure of the trust graph and users’ trust behavior as opposed to other collaborative-filtering based approaches which use ratings of items or user’s profile. We describe an algorithm realizing the approach based on a combination of trust inferences and user similarity, and validate the algorithm using a real large-scale data-set.

Situation-aware trust management

We present a knowledge-intensive and model-based case-based reasoning framework that supports the truster for situation-aware trust management. The suggested method augments the typically sparse trust information by inferring the missing information from other situational conditions, and can better support situation-aware trust management. Our framework can be coupled with existing trust management models to make them situation-aware. It uses the underlying model of trust management to transfer trust information between situations. We validate the proposed framework for Subjective Logic trust management model and evaluate it by conducting experiments on a large real dataset.

Cross-Situation Trust Reasoning

We propose an ontology-based approach for inferences linking trust information in two different situations. That reasoning process can augment the typically sparse trust information, by inferring the missing information from other situational conditions, and can better support situation-aware trust management. Our work is more comprehensive in comparison with other models and considers various aspects of the relationship between situation-awareness and trust management.

Trust 2.0: Who to believe in the flood of online data?

The Internet and the World Wide Web together form a globally distributed network that provides a ubiquitous medium for interaction, the exchange of ideas, and commerce. The web is pervading our everyday lives in ways that were unimaginable even ten years ago. The evolving use of the web requires robust and efficient trust and reputation management mechanisms. Despite numerous proposals on trust and reputation systems, designing a robust and reliable system is still largely an open challenge, especially in the vast social networks that have become popular. A goal of this paper is to describe the critical open challenges that limit the effectiveness of todays trust and reputation systems. We first provide a comprehensive literature review. Moreover, we present a set of our contributions as a way to address some of these challenges. We also propose a future research agenda for trust and reputation systems.