Combination of Evidence in Recommendation Systems Characterized by Distance Functions

Complex Systems Modeling
Modeling, Algorithms, and Informatics Group (CCS-3)
Los Alamos National Laboratory, MS B256
Los Alamos, New Mexico 87545, USA

Citation: Rocha, Luis M. [2002]. "Combination of Evidence in Recommendation Systems Characterized by Distance Functions". In: Proceedings of the 2002 World Congress on Computational Intelligence: FUZZ-IEEE02. Honolulu, Hawaii, May 2002. IEEE Press, pp. 203-208. LAUR 02-154.

The full paper is available in Adobe Acrobat (.pdf) format only. Due to mathematical notation and graphics, only the abstract and introduction are presented here.


Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW), Digital Libarries, or Scientific Databases, often make use of distance functions extracted from relationships among documents and between documents and semantic tags. For instance, documents in the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks. Furthermore, documents can be related to semantic tags such as keywords used to describe their content. The distance functions computed from these relations establish associative networks among items of the DN, and allow recommendation systems to identify relevant associations for individual users. The process of recommendation can be improved by integrating associative data from different sources. Thus we are presented with a problem of combining evidence (about associations between items) from different sources characterized by distance functions. In this paper we summarize our work on (1) inferring associations from semi-metric distance functions and (2) combining evidence from different (distance) associative DN.

1. Recommendation in Document Networks

The prime example of a Document Network (DN) is the World Wide Web (WWW). But many other types of such networks exist: bibliographic databases containing scientific publications, preprints, internal reports, as well as databases of datasets used in scientific endeavors. Each of these databases possesses several distinct relationships among documents and between documents and semantic tags or indices that classify documents appropriately.

DN typically function as information resources for communities of users who query them to obtain relevant information for their activities. Resources such as the Internet, Digital Libraries, and the like have become ubiquitous in the past decade, demanding the development of new techniques to cater to the information needs of communities of users. These techniques come from the field of Information Retrieval, and are typically known as Recommender Systems e.g. [6] [5] [3] [16].

The algorithms we have developed in this area integrate evidence about the association amongst elements of DN, amongst users, and about the interests of individual users and their communities. In particular, a soft computing algorithm (TalkMine) has been created to integrate such evidence and also adapt DN to the expectations of their users [15] . The process of integration of knowledge in TalkMine requires the construction of distance functions on DN that characterize the associations amongst their components. Below we discuss how such distance functions are used to characterize DN and for recommendation.

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Last Modified: September 02, 2004