Trust and Distrust Aggregation Enhanced with Path Length Incorporation

Trust networks are social networks in which users can assign trust scores to each other. In order to estimate these scores for agents that are indirectly connected through the network, a range of trust score aggregators has been proposed. Currently, none of them takes into account the length of the paths that connect users; however, this appears to be a critical factor since longer paths generally contain less reliable information. In this paper, we introduce and evaluate several path length incorporating aggregation strategies in order to strike the right balance between generating more predictions on the one hand and maintaining a high prediction accuracy on the other hand.European Union (EU) TIN2010-17876; TIC-5299; TIC-05991FW

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

Fuzzy Group Decision Making for Influence-Aware Recommendations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works

Market liquidity and its incorporation into risk management.

The excessively optimistic assessment of market liquidity, i.e. the belief that transactions can be settled at current prices without any notable delays or transaction costs, may be a serious threat to financial stability–the near failure of the LTCM hedge fund in 1998 was a case in point. Admittedly, the financial community today appears to have a better grasp of the risks arising from liquidity illusion. The fact nonetheless remains that current risk management tools, particularly the most common Value at Risk (VaR) measures, do not capture this complex component of market risk satisfactorily. In fact, standard VaR calculations do not take specific account of the risk to which a portfolio is exposed at the time it is liquidated. This article aims to explore the different aspects of liquidity risk and provide signposts to methods for incorporating this risk into existing risk control tools. We fi rst examine “normal” or average liquidity risk, which corresponds to the costs of liquidating or hedging a position in tranquil periods, then illiquidity risk that arises in crisis periods and results in the market’s inability to absorb order flows without violent price adjustments. Two separate methodologies, which must nonetheless be combined in a comprehensive approach, are required to analyse these two situations. In the first case we seek to assess the frictions that emerge in imperfect markets by using bid-ask spread measures and by analysing the negative impact on prices resulting from the liquidation of a sizeable portfolio. In the case of extreme risk, we assess the potential consequences of occurrences that are rare, fundamentally uncertain and systemically important. In each case, we suggest and describe a number of techniques that aim to incorporate these elements into the risk measurement and management systems used by private market participants, while underscoring the obstacles to application given the frequent unavailability of the data required. We show that these techniques are relevant because they provide a more cautious and more realistic assessment of financial institutions’ exposure to risk. Lastly, it is in market participants’ own interest for central banks and supervisory bodies to have at their disposal the information required to construct indicators for monitoring market liquidity or conducting suffi ciently comprehensive stress tests in order to assess the fi nancial system’s resilience to liquidity shocks, while taking into account all the externalities that market participants do not individually consider.

Dynamics of public opinions in an online and offline social network

With the development of the information and Internet technology, public opinions with big data will rapidly emerge in an online-offline social network, and an inefficient management of public opinions often will lead to security crises for either firms or governments. To unveil the interaction mechanism among a large number of agents between the online and offline social networks, this paper proposes a public opinion dynamics model in an online-offline social network context. Within a theoretical framework, the analytical conditions to form a consensus in the public opinion dynamics model is investigated. Furthermore, extensive simulations to investigate how the online agents impact the dynamics of public opinion formation are conducted, which unfold that online agents shorten the steady-state time, decrease the number of opinion clusters, and smooth opinion changes in the opinion dynamics. The increase of online agents often enhances these effects. The results in this paper can provide a basis for the management of public opinions in the Internet age