Evolving a multi-agent information filtering solution in Amalthaea

Moukas, Alexandros; Zacharia, Giorgos

doi:10.1145/267658.267747

Cited by 40 publications

(21 citation statements)

References 10 publications

(2 reference statements)

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“…Ripper [8] and RE:Agent [9] use learning techniques to classify e-mail based on a user's prior actions. Amalthaea [10] is a multiagent system for recommending information sources on the Internet. Information filtering agents keep track of a user's interests while information discovery agents search and retrieve documents matching the user's interest profile.…”

Section: Recommender System Applicationsmentioning

confidence: 99%

View through MetaLens: usage patterns for a meta-recommendation system

2004

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In a world where a person's number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries, and knowledge management.In this paper, we address such systems, as well as a relatively new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalized control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. We discuss observations made from the public trial of a meta-recommender system in the domain of movies, and lessons learned from the incorporation of features that allow persistent personalization of the system. Finally, we consider the challenges of building real-world, usable meta-recommenders across a variety of domains.

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Section: Recommender System Applicationsmentioning

confidence: 99%

View through MetaLens: usage patterns for a meta-recommendation system

2004

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“…SIFTER [7] and Syskill & Webert [9] update a user's pro le based on the user's evaluation of the data accessed, and Amalthaea [8] applies the method of combining autonomous agents and arti cial life in the creation of an evolving ecosystem composed of competing and cooperating agents. Since the ltering policy of these systems changes in time, the ltering function in this paper can not represent their properties.…”

Section: Application To Related Workmentioning

confidence: 99%

On functional properties of information filtering

Sawai

Tsukamoto

Terada

et al. 2003

Electron Comm Jpn Pt II

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In recent years, due to the increasing popularization of data broadcasting, the volume and variety of data being broadcast are rapidly increasing. In this environment, as it is di cult for users to search for information from a large amount of broadcast data, there is an increasing demand for ltering techniques that automatically extract only the necessary data. Consequently, a number of ltering methods have been proposed. However, mathematical representation of these methods does not exist. T h u s , i t i s n o t possible to qualitatively evaluate various ltering methods, optimize processing methods in ltering, or design a declarative language for ltering processes. In this paper, we de ne ltering as a function, and express the properties of ltering methods by the constraints satis ed by this function. By showing the inclusion relation of constraints representing the properties of ltering, we clarify the relationship between the properties of ltering. Using the framework proposed in this paper, we are able to categorize the actual ltering systems. By applying the appropriate processing method for each category according to its properties, more e cient ltering systems can be achieved.Permission to copy without fee all or part of this material is granted p r ovided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice i s given that copying is by permission of the Very Large Data Base Endowment. To c opy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

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“…Fab (Balabanović, 1997) and Amalthaea (Moukas & Zacharia, 1997) are multi-agent adaptive filtering systems inspired by genetic algorithms, artificial life, and market models. Term weighting and relevance feedback are used to adapt a matching between a set of discovery agents (typically search engine parasites) and a set of user profiles (corresponding to single-or multiple-user interests).…”

Section: Other Related Projectsmentioning

confidence: 99%

Untitled

2000

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Abstract. This paper discusses a novel distributed adaptive algorithm and representation used to construct populations of adaptive Web agents. These InfoSpiders browse networked information environments on-line in search of pages relevant to the user, by traversing hyperlinks in an autonomous and intelligent fashion. Each agent adapts to the spatial and temporal regularities of its local context thanks to a combination of machine learning techniques inspired by ecological models: evolutionary adaptation with local selection, reinforcement learning and selective query expansion by internalization of environmental signals, and optional relevance feedback. We evaluate the feasibility and performance of these methods in three domains: a general class of artificial graph environments, a controlled subset of the Web, and (preliminarly) the full Web. Our results suggest that InfoSpiders could take advantage of the starting points provided by search engines, based on global word statistics, and then use linkage topology to guide their search on-line. We show how this approach can complement the current state of the art, especially with respect to the scalability challenge.

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Evolving a multi-agent information filtering solution in Amalthaea

Cited by 40 publications

References 10 publications

View through MetaLens: usage patterns for a meta-recommendation system

View through MetaLens: usage patterns for a meta-recommendation system

On functional properties of information filtering

Untitled

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