Miquel Montaner

A Taxonomy of Recommender Agents on theInternet

Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.

Collaboration Analysis in Recommender Systems Using Social Networks

Many researchers have focused their efforts on developing collaborative recommender systems. It has been proved that the use of collaboration in such systems improves performance, but what is not known is how this collaboration is done and what is more important, how it has to be done in order to optimise the information exchange. The collaborative relationships in recommender systems can be represented as a social network. In this paper we propose several measures to analyse collaboration based on social network analysis. Once these measures are explained, we use them to evaluate a concrete example of collaboration in a real recommender system.

Opinion-Based Filtering through Trust

Recommender systems help users to identify particular items that best match their tastes or preferences. When we apply the agent theory to this domain, a standard centralized recommender system becomes a distributed world of recommender agents. Therefore, due to the agents world, a new information filtering method appears: the opinion-based filtering method. Its main idea is to consider other agents as personal entities which you can rely on or not. Recommender agents can ask their reliable friends for an opinion about a particular item and filter large sets of items based on it. Reliability is expressed through a trust value with which each agent labels its neighbors. Thus, the opinion-based filtering method needs a model of trust in the collaborative world. The model proposed emphasizes proactiveness since the agent looks for other agents in a situation of lack of information instead of remaining passive or providing either a negative or empty answer to the user. Finally, our social model of trust exploits interactiveness while preserving privacy.

Developing trust in recommender agents

Trust is one of the most important social concepts that helps human agents to cope with their social environment and is present in all human interaction. Like in real world, agents should rely in some agents and mistrust in other ones to achieve a purpose. In this paper we develop a model of trust in the collaborative world as a new approach of recommender agents development. Mainly, we provide recommender agents with a technology to look for similar agents that advice him. The model presented comprehends the evolution of trust, that is, trust dynamics. Moreover, the model proposed emphasizes proactiveness since the agent looks for other agents in a lack for information situation instead of remaining passive or providing either a negative or empty answer to the user.

Embedding Emotional Context in Recommender Systems

Emotions are crucial for users decision making in recommendation processes. We first introduce ambient recommender systems, which arise from the analysis of new trends on the exploitation of the emotional context in the next generation of recommender systems. We then explain some results of these new trends in real-world applications through the smart prediction assistant (SPA) platform in an intelligent learning guide with more than three million users. While most approaches to recommending have focused on algorithm performance. SPA makes recommendations to users on the basis of emotional information acquired in an incremental way. This article provides a cross-disciplinary perspective to achieve this goal in such recommender systems through a SPA platform. The methodology applied in SPA is the result of a bunch of technology transfer projects for large real-world rccommender systems.

Improving Case Representation and Case Base Maintenance in Recommender Agents

Recommendations bysalesp eople are always based on knowledge about the products and expertise about your tastes, preferences, interests and behavior in the shop. In an attempt to model the behavior of salespeople, AI research has been focussed on the so called recommender agents. Such agents draw on previous results from machine learning and other advances in AI technologyto develop user models and to anticipate and predict user preferences. In this paper we introduce a new approach to recommendation, based on Case-Based Reasoning (CBR). CBR is a paradigm for learning and reasoning through experience, as salesmen do. We present a user model based on cases in which we try to capture both explicit interests (the user is asked for information) and implicit interests (captured from user interaction) of a user on a given item. Retrieval is based on a similarityfunction that is constantlytuned according to the user model. Moreover, in order to cope with the utility problem that current CBR system suffer from, our approach includes a forgetting mechanism (the drift attribute) that can be extended to other applications beyond e-commerce.

A Negotiation-Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality but also with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems (DBEs), as well as related services like open-id, trust management, monitors, and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up ONEs that are stable, a basic condition for predictable and reliable business environments. Aiming to build stable DBEs by means of improved collective intelligence, we introduce a model of negotiation-style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor and a novel negotiation-style recommender (NSR). The ecosystem monitor provides hints to the NSR to achieve greater stability of ONE in a DBE. The greater stability provides the small companies with higher predictability and, therefore, better business results. The NSR is implemented with a simulated-annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of ONE populated by Italian companies.

RoGi Team Description

This paper describes the main features of the new Rogi Team and some research applied, focused on dynamics of physical agents. It explains the vision system, the control system and the robots, so that the research on dynamical physical agents could be performed. It presents part of the research done in physical agents, especially consensus of properly physical decisions among physical agents, and an example applied to passing.

Preliminary Studies of Dynamics of Physical Agent Ecosystems

This paper is a step forward from the agent ecosystems that Hogg studied [6]. We plan to extend these agents ecosystems to physical agents that interact with the physical world. The aim is to conceive algorithms for the choice of resources and to expand this work. Dynamics of choice in such ecosystems depends on pay-off functions that contain information about the real physical world. One contribution here is to formalise the choice of knowledge resources by including a consensus technique. The second contribution is to include diversity by means of physical agents and to analyse the emergent impact in terms of diversity and performance. Simulated soccer robots exemplifies all this.

From physical agents to recommender agents

In this paper, we argue in favour of the interchange of research results between physical agents and recommender systems. Certain properties, such as introspection and physical foundations of physical agents, are mapped into agents that model and make recommendations to users from the subjective, content-based point of view of products and services. How this real-to-virtual mapping of properties and behaviours can be performed is the main goal of our work, from which we expect to obtain a general concept of recommender agents which improves the state of the art in terms of performance and maintainability. Furthermore, new features of the recommender agents could be also mapped to the physical agents discipline. Some of the features explained are installed in two personalization company products: Proto-agent and Habitat-Pro.