Evaluating the informative quality of documents in SGML format from judgements by means of fuzzy linguistic techniques based on computing with words

Recommender systems evaluate and filter the great amount of information available on the Web to assist people in their search processes. A fuzzy evaluation method of Standard Generalized Markup Language documents based on computing with words is presented. Given a document type definition (DTD), we consider that its elements are not equally informative. This is indicated in the DTD by defining linguistic importance attributes to the more meaningful elements of DTD chosen. Then, the evaluation method generates linguistic recommendations from linguistic evaluation judgements provided by different recommenders on meaningful elements of DTD. To do so, the evaluation method uses two quantifier guided linguistic aggregation operators, the linguistic weighted averaging operator and the linguistic ordered weighted averaging operator, which allow us to obtain recommendations taking into account the fuzzy majority of the recommenders’ judgements. Using the fuzzy linguistic modeling the user–system interaction is facilitated and the assistance of system is improved. The method can be easily extended on the Web to evaluate HyperText Markup Language and eXtensible Markup Language documents.