Domenico Rosaci

Integrating trust measures in multiagent systems

Several models have been proposed in the past for representing both reliability and reputation. However, we remark that a crucial point in the practical use of these two measures is represented by the possibility of suitably combining them to support the agents decision. In the past, we proposed a reliability–reputation model, called RRAF, that allows the user to choose how much importance to give to the reliability with respect to the reputation. However, RRAF shows some limitations, namely: (i) The weight to assign to the reliability versus reputation is arbitrarily set by the user, without considering the system evolution; (ii) the trust measure that an agent a perceives about an agent b is completely independent of the trust measure perceived by each other agent c, while in the reality the trust measures are mutually dependent. In this paper, we propose an extension of RRAF, aiming at facing the limitations above. In particular, we introduce a new trust reputation model, called TRR, that considers, from a mathematical viewpoint, the interdependence among all the trust measures computed in the systems. Moreover, this model dynamically computes a parameter measuring the importance of the reliability with respect to the reputation. Some experiments performed on the well‐known ART(Agent Reputation and Trust) platform show the significant advantages in terms of effectiveness introduced by TRR with respect to RRAF.

Recommending multimedia web services in a multi-device environment

In the last years, the Web community has shown a broad interest in Web services that handle multimedia contents. To improve the usability of these services different tools have been proposed in the literature, and in this context agent-based recommender systems appear to be a promising solution. However, the recommender systems presented in the past do not take into account, in their recommendation algorithms, the effect of the device exploited by the user, while it is clear that the same user shows a different behavior in the presence of different devices. This paper tries to give a contribution in this setting, in order to match more accurately user preferences and interests. In particular, a new agent-based system is proposed, whose architecture allows to compute recommendations of multimedia Web services, considering the effect of the currently exploited device. Some experimental results confirm the high quality of the recommendations generated by the proposed approach.

Modeling cooperation in multi-agent communities

In Multi-Agent Systems the main goal is providing fruitful cooperation among agents in order to enrich the support given to user activities. Cooperation can be implemented in many ways, depending on how local knowledge of agents is represented and consists, in general, in providing the user with an integrated view of individual knowledge bases. But the main difficulty is determining which agents are promising candidates for a fruitful cooperation among the (possibly large) universe of agents operating in the net. This paper gives a contribution in this context, by proposing a formal framework for representing and managing cooperation in multi-agent networks. Semantic properties are here represented by coefficients and adaptive algorithms permit the computation of a set of agents suggested for cooperation. Actual choices of the users modify internal parameters in such a way that the next suggestions are closer to users expectancy.

MASHA: A multi-agent system handling user and device adaptivity of Web sites

A user that navigates on the Web using different devices should be characterized by a global profile, which represents his behaviour when using all these devices. Then, the user’s profile could be usefully exploited when interacting with a site agent that is able to provide useful recommendations on the basis of the user’s interests, on one hand, and to adapt the site presentation to the device currently exploited by the user, on the other hand. However, it is not suitable to construct such a global profile by a software running on the exploited device since this device (e.g., a mobile phone or a palmtop) may have limited resources. Therefore, in this paper, we propose a multi-agent architecture, called MASHA, handling user and device adaptivity of Web sites, in which each device is provided with a client agent that autonomously collects information about the user’s behaviour associated to just that device. However, the user profile contained in this client is continuously updated with information coming from a unique server agent, associated with the user. Such information is collected by the server agent from the different devices exploited by the user, and represents a global user profile. The third component of this architecture, called adapter agent, is capable to generate a personalized representation of the Web site, containing some useful recommendations derived by both an analysis of the user profile and the suggestions coming from other users exploiting the same device.

MUADDIB: A distributed recommender system supporting device adaptivity

Web recommender systems are Web applications capable of generating useful suggestions for visitors of Internet sites. However, in the case of large user communities and in presence of a high number of Web sites, these tasks are computationally onerous, even more if the client software runs on devices with limited resources. Moreover, the quality of the recommendations strictly depends on how the recommendation algorithm takes into account the currently used device. Some approaches proposed in the literature provide multidimensional recommendations considering, besides items and users, also the exploited device. However, these systems do not efficiently perform, since they assign to either the client or the server the arduous cost of computing recommendations. In this article, we argue that a fully distributed organization is a suitable solution to improve the efficiency of multidimensional recommender systems. In order to address these issues, we propose a novel distributed architecture, called MUADDIB, where each users device is provided with a device assistant that autonomously retrieves information about the users behavior. Moreover, a single profiler, associated with the user, periodically collects information coming from the different users device assistants to construct a global users profile. In order to generate recommendations, a recommender precomputes data provided by the profilers. This way, the site manager has only the task of suitably presenting the content of the site, while the computation of the recommendations is assigned to the other distributed components. Some experiments conducted on real data and using some well-known metrics show that the system works more effectively and efficiently than other device-based distributed recommenders.

A multi-agent recommender system for supporting device adaptivity in e-Commerce

Traditional recommender systems for e-Commerce support the customers’ activities providing them with useful suggestions about available products in Web stores. To this purpose, in an agent-based context, each customer is often associated with a customer agent that interacts with the site agent associated with the visited e-Commerce Web site. In presence of a high number of interactions between customers and Web sites, the generation of recommendations can be a heavy task for both these agents. Moreover, customers can navigate on the Web by using different devices having different characteristics that may influence customer’s preferences. In this paper we propose a new multi-agent system, called ARSEC, where each device exploited by a customer is associated with a device agent that autonomously monitors his/her behaviour. Furthermore, each customer is associated with a customer agent that collects in a global profile the information provided by his/her device agents and each e-Commerce Web site is associated with a seller agent. Based on the similarity existing among the global profiles the customers are partitioned in clusters, each one managed by a counsellor agent. Recommendations are generated in ARSEC as result of the collaboration between the seller agent and some counsellor agents associated with the customer. The usage of the device agents leads to generating recommendations taking into account the device currently used, while the fully decentralized architecture introduces a strong reduction of the time costs. Some experimental results are presented to show the significant advantages obtained by ARSEC in terms of recommendation effectiveness with respect to other well-known agent-based recommenders.

Recommendation of reliable users, social networks and high-quality resources in a Social Internetworking System

Social Internetworking Systems are a significantly emerging new reality; they group together some social networks and allow their users to share resources, to acquire opinions and, more in general, to interact, even if these users belong to different social networks and, therefore, did not previously know each other. In this context, owing to the huge dimension of existing social networks, the capability of a Social Internetworking System to provide its users with recommendations of reliable users and social networks, as well as of high-quality resources, is extremely relevant. In the past, user and resource recommendation has been investigated in the context of a single social network, whereas it has still received a little attention in the context of a Social Internetworking System, owing to the novelty of this phenomenon. For the same reason, social network recommendation has received an even less attention. In this paper we propose a trust-based approach to face these challenges. Specifically, we introduce a model to represent and handle trust and reputation in a Social Internetworking System and propose an approach that exploits these parameters to compute the reliability of a user or a social network, as well as the quality of a resource. These last measures are then exploited to perform recommendations.

Multi-agent technology and ontologies to support personalization in B2C E-Commerce

In this paper we present an XML-based multi-agent system, called Multi Agent System for Traders (MAST), that supports several Business-to-Customer e-Commerce activities, including advertisements and payments. MAST helps both customers and merchants in performing their tasks by using a personalized approach. MASTs e-payment model avoids exchanging sensitive information, reinforcing trust between merchants and customers. A complete prototype of MAST has been implemented under the JADE framework, and it has been exploited for realizing some experiments, in order to evaluate its performances.

Trust and Compactness in Social Network Groups

Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is particularly relevant. While the literature usually refers to compactness as a measure to merely determine how much members of a group are similar among each other, we argue that the mutual trustworthiness between the members should be considered as an important factor in defining such a term. In fact, trust has profound effects on the dynamics of group formation and their evolution: individuals are more likely to join with and stay in a group if they can trust other group members. In this paper, we propose a quantitative measure of group compactness that takes into account both the similarity and the trustworthiness among users, and we present an algorithm to optimize such a measure. We provide empirical results, obtained from the real social networks EPINIONS and CIAO, that compare our notion of compactness versus the traditional notion of user similarity, clearly proving the advantages of our approach.

EFFICIENT PERSONALIZATION OF E‐LEARNING ACTIVITIES USING A MULTI‐DEVICE DECENTRALIZED RECOMMENDER SYSTEM

Personalization is becoming a key issue in designing effective e‐learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e‐learning site. However, in presence of a large number of students and of e‐learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the students behavior when accessing e‐learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e‐learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations. We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders.