Intelligent Collaborative Information Retrieval

Delgado, Joaquin; Ishii, Naohiro; Ura, Tomoki

doi:10.1007/3-540-49795-1_15

Cited by 9 publications

(5 citation statements)

References 2 publications

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“…[Mladenić and Stefan 1999] provides a good survey of text-learning and agent systems, including content-based and collaborative approaches. The systems most related to Quickstep and Foxtrot are Entrée [Burke 2000], which uses a knowledge base and case-based reasoning to recommend restaurant data, and RAAP [Delgado et al 1998] that uses simple categories to represent user profiles with unshared individual training sets for each user. None of these systems use an ontology to explicitly represent user profiles.…”

Section: Related Workmentioning

confidence: 99%

Ontological user profiling in recommender systems

Middleton

Shadbolt

Roure

2004

ACM Trans. Inf. Syst.

569

289

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________________________________________________________________________We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

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Section: Related Workmentioning

confidence: 99%

Ontological user profiling in recommender systems

Middleton

Shadbolt

Roure

2004

ACM Trans. Inf. Syst.

569

289

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“…Most similar to our work is the RAAP system [5]. In RAAP the system also learns by using a classical classifier how users classify bookmarks and use this information to recommend people with new bookmarks.…”

Section: Related Workmentioning

confidence: 99%

“…The GAB system can automatically merge different user's bookmark lists in a single and a seamless hierarchy [25]. RAPP provides users with personalised help for bookmark classification and group-based bookmark recommendation service [5]. Other systems allow to build and to identify communities of interest by analysing user's bookmark collections.…”

mentioning

confidence: 99%

A multi-agent system for collaborative bookmarking

Kanawati¹,

Malek

2002

Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems Part 3 - AAMAS '02

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Abstract. In this paper we describe a multi-agent system, called COWING that aims at enabling an organised group of people to share their results of information searching on the World Wide Web. Users normally save relevant sites they found in their private collections of bookmarks. The goal of the proposed system is to enable users to share their bookmarks, in an implicit, secure and effective way. By implicit we mean that users are not required to spend extra effort in order to use the system. Secure sharing refers to the capacity of each user to control who knows what about her/his own bookmark collections. Finally effectiveness is ensured by recommending users with relevant bookmarks that are computed by applying a distributed collaborative filtering algorithm. Recommended bookmarks are automatically inserted in the most appropriate bookmark folder in the user's local collection. When accessing a folder the user can evaluate (accept or reject) provided recommendations. Our system is a multi-agent system where each user is associated to an assistant agent, called a WING agent. A WING agent observes the user's behaviour when managing her/his own bookmark collection. It learns how the user classifies her/his bookmarks. A hybrid neural/case-based reasoning supervised classifier is used for this purpose. WING agents exchange bookmarks according to a defined collaboration protocol that protects associated users privacy.Keywords: Collaborative Information Agents, Bookmark, Hybrid Neural/CBR classification. 1.À IntroductionThe most intelligent agents that browse and index sites on the World Wide Web (the Web hereafter) still to be humans. Consequently, an effective way to locate relevant information on the Web is to locate people who are likely to know where to find the searched information [25]. Users usually store addresses of relevant Web sites in a bookmark directory. Almost all Web browsers available today provide users with some bookmaking facility. Typically a bookmark is a record that holds the information like: the site address (i.e. URL), the site title (i.e. the title of the indexed 84 page), and some other data such as bookmark creation date, last visit date and userprovided description of the indexed page. The bookmark set becomes a personal web space that helps the user in land-marking the huge information space composed of the currently available web pages. A major reason behind the popularity of bookmarks is their ease of use. In most browsers a simple click is enough to save the currently visited web page into the list of bookmarks. Later, another simple click on the saved bookmark takes the user directly to the required site. In order to enhance access time to saved bookmarks, most existing bookmarking tools allow users to organise their collections in a hierarchy of folders. While this may effectively enhance the access time, it introduces new problems: "users must continually trade-off the cost of organising their bookmarks and remembering which bookmark are in which folder versus the cost of havi...

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“…Different learning and adaptation techniques are integrated with SIGMA for creating a robust network‐based application, which adapts to both changes in the characteristics of the information available on the network as well as to changes in individual user's information interests. The Research Assistant Agent Project (RAAP; Delgado, Ishii, & Ura, 1998) is devoted to supporting collaborative research by classifying domain specific information retrieved from the Web, and recommending these “bookmarks” to other researcher with similar research interests.…”

Section: Introductionmentioning

confidence: 99%

Distributed multi‐agent information filtering—A comparative study

Mukhopadhyay¹,

Peng²,

Raje³

et al. 2005

J. Am. Soc. Inf. Sci.

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Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large-scale information infrastructure, e.g., digital libraries. To provide large-scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single-agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium-scale applications. In this article, we discuss two distributed (multiagent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single-agent centralized information filtering. Large-scale experimental studies involving the well-known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches.

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Intelligent Collaborative Information Retrieval

Cited by 9 publications

References 2 publications

Ontological user profiling in recommender systems

Ontological user profiling in recommender systems

A multi-agent system for collaborative bookmarking

Distributed multi‐agent information filtering—A comparative study

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