Automatic construction of multi-faceted user profiles using text clustering and its application to expert recommendation and filtering problems
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AuthorCampos Ibáñez, Luis Miguel; Fernández Luna, Juan Manuel; Huete Guadix, Juan Francisco; Redondo-Expósito, Luis
Cluster analysisContent-based recommendationExpert findingFiltering algorithmUser profiles
Knowledge-Based Systems 190 (2020) 105337
SponsorshipThis work has been funded by the Spanish Ministerio de Economía y Competitividad under project TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER).
In the information age we are living in today, not only are we interested in accessing multimedia objects such as documents, videos, etc. but also in searching for professional experts, people or celebrities, possibly for professional needs or just for fun. Information access systems need to be able to extract and exploit various sources of information (usually in text format) about such individuals, and to represent them in a suitable way usually in the form of a profile. In this article, we tackle the problems of profile-based expert recommendation and document filtering from a machine learning perspective by clustering expert textual sources to build profiles and capture the different hidden topics in which the experts are interested. The experts will then be represented by means of multifaceted profiles. Our experiments show that this is a valid technique to improve the performance of expert finding and document filtering.