Ana Peleteiro

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

With the advent of new cable and satellite services, and the next generation of digital TV systems, people are faced with an unprecedented level of program choice. This often means that viewers receive much more information than they can actually manage, which may lead them to believe that they are missing programs that could likely interest them. In this context, TV program recommendation systems allow us to cope with this problem by automatically matching users likes to TV programs and recommending the ones with higher user preference. This paper describes the design, development, and startup of queveo.tv: a Web 2.0 TV program recommendation system. The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc. To eliminate the most serious limitations of collaborative filtering, we have resorted to a well-known matrix factorization technique in the implementation of the item-based collaborative filtering algorithm, which has shown a good behavior in the TV domain. Every step in the development of this application was taken keeping always in mind the main goal: to simplify as much as possible the user task of selecting what program to watch on TV.

moreTourism: Mobile recommendations for tourism

This paper introduces a hybrid recommendation platform providing information about tourist resources depending on the user profile, location, schedule and the amount of time for visiting interest points isolated or combined in a route.

Fostering Cooperation through Dynamic Coalition Formation and Partner Switching

In this article we tackle the problem of maximizing cooperation among self-interested agents in a resource exchange environment. Our main concern is the design of mechanisms for maximizing cooperation among self-interested agents in a way that their profits increase by exchanging or trading with resources. Although dynamic coalition formation and partner switching (rewiring) have been shown to promote the emergence and maintenance of cooperation for self-interested agents, no prior work in the literature has investigated whether merging both mechanisms exhibits positive synergies that lead to increase cooperation even further. Therefore, we introduce and analyze a novel dynamic coalition formation mechanism, that uses partner switching, to help self-interested agents to increase their profits in a resource exchange environment. Our experiments show the effectiveness of our mechanism at increasing the agents’ profits, as well as the emergence of trading as the preferred behavior over different types of complex networks.

A transport based clearing system for dynamic carpooling business services

The WiSafeCar (Wireless Traffic Safety Network Between Cars) project aims at creating a comprehensive wireless communication and service platform targeting vehicular networks, striving to reduce accidents and traffic congestion. Within the frame of this project, a dynamic carpooling transport system was designed, reacting in real time to events and user transport requests. In order to allow this system to integrate seamlessly with other transport modes, but also to encourage users to use carpooling, a clearing service has been implemented to solve the inherent compensation issues. Both the carpooling system and the underlying utility services have been prototyped, using the Netlogo simulator, to prove not only the efficiency of WiSafeCar at reducing the congestion in cities; but also to validate the business models to be implemented and more precisely the clearing algorithm.

Learning to cooperate in the Iterated Prisoner's Dilemma by means of social attachments

The Iterated Prisoner’s Dilemma (IPD) has been used as a paradigm for studying the emergence of cooperation among individual agents. Many computer experiments show that cooperation does arise under certain conditions. In particular, the spatial version of the IPD has been used and analyzed to understand the role of local interactions in the emergence and maintenance of cooperation. It is known that individual learning leads players to the Nash equilibrium of the game, which means that cooperation is not selected. Therefore, in this paper we propose that when players have social attachment, learning may lead to a certain rate of cooperation. We perform experiments where agents play the spatial IPD considering social relationships such as belonging to a hierarchy or to coalition. Results show that learners end up cooperating, especially when coalitions emerge.

Evolutionary Algorithms Based on Game Theory and Cellular Automata with Coalitions

Cellular genetic algorithms (cGAs) are a kind of genetic algorithms (GAs) with decentralized population in which interactions among individuals are restricted to the closest ones. The use of decentralized populations in GAs allows to keep the population diversity for longer, usually resulting in a better exploration of the search space and, therefore in a better performance of the algorithm. However, the use of decentralized populations supposes the need of several new parameters that have a major impact on the behavior of the algorithm. In the case of cGAs, these parameters are the population and neighborhood shapes. Hence, in this work we propose a new adaptive technique based in Cellular Automata, Game Theory and Coalitions that allow to manage dynamic neighborhoods. As a result, the new adaptive cGAs (EACO) with coalitions outperform the compared cGA with fixed neighborhood for the selected benchmark of combinatorial optimization problems.

Emerging Cooperation in the Spatial IPD with Reinforcement Learning and Coalitions

Game theory provides useful mathematical tools to understand the possible strategies that self-interested agents may follow when choosing an action. The context of evolution of cooperation has been extensively studied seeking general theoretical frameworks like the Prisoner’s Dilemma (PD) [2]. In his seminal work, Axelrod has shown that cooperation can emerge in a society of individuals with selfish motivations. Since that, Game Theory and the Prisoner’s Dilemma have been applied in biological, social, economical and ecological contexts. An interesting spatial version of the PD has been suggested and deeply analyzed in [22] trying to understand the role of local interactions in the emergence and maintenance of cooperation.

How Coalitions Enhance Cooperation in the IPD over Complex Networks

Grouping agents into coalitions can be an important way of cooperation through which multi-agent systems(MAS) improve their performance, accomplish their assignments, increase their benefits, and achieve their goals. However, agents belonging to a coalition must decide how they behave to increase their gains. In this paper, we present a model that combines learning and coalition formation to enhance cooperation over the Iterated Prisoners Dilemma (IPD). We focus on agents interacting over complex networks since they provide a realistic model of the nowadays interconnected world. Our results suggest that coalitions are a relevant contribution to achieve cooperation in the IPD.

Ownership and trade in spatial evolutionary memetic games

In this paper we consider several strategies to compete in a spatial version of the Iterated Prisoners Dilemma (IPD). The cell interaction is modeled by a two-dimensional square lattice, where each cell only locally interacts with its neighbors. Cell actions are public and therefore can be imitated by neighbors. The main contribution of the paper is the framework for the memetic analysis of the population evolution in this extended version of the spatial prisoners dilemma. Among the classical strategies, cooperate (C) and defect (D), we consider two other strategies based on the property of resources: Possession (P), as the right to possess what one owns, and Trade (T), as the right to buy and sell ownership. This work also includes a set of simulation results showing how ownership and trade emerge from anarchy, as evolutionary stable strategies, to enable the peaceful resolution of property conflicts under certain environment conditions.

USING TAGS IN AN AIML-BASED CHATTERBOT TO IMPROVE ITS KNOWLEDGE

Nowadays, it is common to find on the Internet different conversational robots which interact with users simulating a natural language conversation. Among them, we can emphasize the chatterbots based on AIML language. In this paper we present an AIML based chatterbot that shows as its main contribution the use of tags and folksonomies. Thanks to its use, we can generate a context for each conversation, being able to maintain a state for each user in the system, and improving the adaptation capabilities of the bot.