Josep Lluís de la Rosa

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.

A Survey on Interval Model Simulators and their Properties Related to Fault Detection

Abstract The imprecision and the uncertainty of many systems can be expressed with interval models. The results of the simulation of these models can be represented by envelopes. These envelopes can be characterised by several properties such as completeness or soundness that lead to the concepts of overbounded and underbounded envelopes. The simulation of such interval models can be performed by several means including qualitative and semiqualitative methods as well as quantitative simulation based ones. A brief description of the different types of simulators is presented and their respective properties are outlined and compared in relation to model-based fault detection.

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.

Concept-based learning of human behavior for customer relationship management

In this paper, we apply concept learning techniques to solve a number of problems in the customer relationship management (CRM) domain. We present a concept learning technique to tackle common scenarios of interaction between conflicting human agents (such as customers and customer support representatives). Scenarios are represented by directed graphs with labeled vertices (for communicative actions) and arcs (for temporal and causal relationships between these actions and their parameters). The classification of a scenario is performed by comparing a partial matching of its graph with graphs of positive and negative examples. We illustrate machine learning of graph structures using the Nearest Neighbor approach and then proceed to JSM-based concept learning, which minimizes the number of false negatives and takes advantage of a more accurate way of matching sequences of communicative actions. Scenario representation and comparative analysis techniques developed herein are applied to the classification of textual customer complaints as a CRM component. In order to estimate complaint validity, we take advantage of the observation [19] that analyzing the structure of communicative actions without context information is frequently sufficient to judge how humans explain their behavior, in a plausible way or not. This paper demonstrates the superiority of concept learning in tackling human attitudes. Therefore, because human attitudes are domain-independent, the proposed concept learning approach is a good compliment to a wide range of CRM technologies where a formal treatment of inter-human interactions is required.

Inferring the semantic properties of sentences by mining syntactic parse trees

We extend the mechanism of logical generalization toward syntactic parse trees and attempt to detect semantic signals unobservable in the level of keywords. Generalization from a syntactic parse tree as a measure of syntactic similarity is defined by the obtained set of maximum common sub-trees and is performed at the level of paragraphs, sentences, phrases and individual words. We analyze the semantic features of this similarity measure and compare it with the semantics of traditional anti-unification of terms. Nearest-Neighbor machine learning is then applied to relate the sentence to a semantic class. By using a syntactic parse tree-based similarity measure instead of the bag-of-words and keyword frequency approaches, we expect to detect a subtle difference between semantic classes that is otherwise unobservable. The proposed approach is evaluated in three distinct domains in which a lack of semantic information makes the classification of sentences rather difficult. We conclude that implicit indications of semantic classes can be extracted from syntactic structures.

Soccer team based on agent-oriented programming

In this paper the analysis, design and implementation of a soccer team of micro-robots is explained. Besides the technical difficulties to develop these micro-robots, this paper also shows how to develop a multi-agent co-operative system by means of Matlab/Simulink (MATLAB and SIMULINK are Trade Marks of Math Works. Windows 95 is a Trade Mark of Microsoft Corporation.) a widely known computer aided control system design framework. Agent-oriented paradigms (AOPs) formalise interactions between multiple agents in terms of changing their mental states by communication between agents. Their practical implementations are usually conceived by means of object-oriented paradigms. Nevertheless, the implementation of agent-oriented paradigms in Matlab/Simulink is not straightforward. Thus, the obtained real implementation is an integrated system that includes several programming paradigms so as hardware platforms. Finally, the proposal of the integrated framework for the micro-robots soccer team is shown.

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.

Using generalization of syntactic parse trees for taxonomy capture on the web

We implement a scalable mechanism to build a taxonomy of entities which improves relevance of search engine in a vertical domain. Taxonomy construction starts from the seed entities and mines the web for new entities associated with them. To form these new entities, machine learning of syntactic parse trees (syntactic generalization) is applied to form commonalities between various search results for existing entities on the web. Taxonomy and syntactic generalization is applied to relevance improvement in search and text similarity assessment in commercial setting; evaluation results show substantial contribution of both sources.