Multi-topic information filtering with a single user profile

Nanas, Nikolaos; Uren, Victoria; De Roeck, Anne and Domingue, John (2004). Multi-topic information filtering with a single user profile. In: Methods and Applications of Artificial Intelligence (Vouros, George A. and Panayiotopoulos, Themistoklis eds.), Lecture Notes in Computer Science, Springer, Berlin, pp. 400–409.

DOI: https://doi.org/10.1007/b97168

Abstract

In Information Filtering (IF) a user may be interested in several topics in parallel. But IF systems have been built on representational models derived from Information Retrieval and Text Categorization, which assume independence between terms. The linearity of these models results in user profiles that can only represent one topic of interest. We present a methodology that takes into account term dependencies to construct a single profile representation for multiple topics, in the form of a hierarchical term network. We also introduce a series of non-linear functions for evaluating documents against the profile. Initial experiments produced positive results.

Viewing alternatives

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

Recommendations