In (Fuzzy Sets and Systems 97 (1998) 33), we presented a fuzzy multipurpose decision making model integrating di4erent preference representations based on additive reciprocal fuzzy preference relations. The main aim of this paper is to complete the decision model studying conditions under which reciprocity property is maintained when aggregating preference relations using an OWA operator guided...

We address the problem of learning preference relations over multiattribute (or combinatorial) domains. We do so by making hypotheses about the dependence structure between attributes that the preference relation enjoys. The first hypothesis we consider is the simplest one, namely, separability (no dependences between attributes: the preference over the values of each attribute is independent o...

Collaborative filtering-based recommender systems exploit user preferences about items to provide them with recommendations. These preferences are generally ratings. However, choosing a rating is no easy task for any user; the rating scale is usually reduced and the rating values given by the users may be influenced by many factors. The ratings are thus not completely trustworthy. This paper is...

Article history: Received 12 June 2009 Received in revised form 1 August 2010 Accepted 3 August 2010

The composition of fuzzy relations for their uniting in multicriteria decision making problems is considered. Some criteria evaluate a set of alternatives by fuzzy preference relations. The comparison between the criteria importance is given as a fuzzy preference relation too. Aggregation of these relations is made with the help of a composition of the couple of the relations by the criteria an...

Supplier selection is usually involved a multi-criteria evaluation to assist decision-making. This study proposed a simple method for vendor selection. The proposed procedure utilizes the structure of criteria in analytic hierarchy process (AHP) model and employs consistent fuzzy preference relations (CFPR) to construct the decision matrices. The advantage of CFPR is computational simplicity an...

Preference learning has received increasing attention in both machine learning and information retrieval. The goal of preference learning is to automatically learn a model to rank entities (e.g., documents, webpages, products, music, etc.) according to their degrees of relevance. The particularity of preference learning might be that the training data is a set of pairwise preferences between en...

The problem of nding the solution set of alternatives when a linguistic preference relation represents a collective preference is analyzed. Three choice sets for a linguistic preference relation are studied and some relations between them are pointed out. The concept of linguistic choice function is introduced and some particular cases are analyzed.

New updated! The latest book from a very famous author finally comes out. Book of uncertain fuzzy preference relations and their applications, as an amazing reference becomes what you need to get. What's for is this book? Are you still thinking for what the book is? Well, this is what you probably will get. You should have made proper choices for your better life. Book, as a source that may inv...

A standard way of presenting the semantics of default information is via preference relations on models. Under this view, the default information gives rise to a preference relation which orders models according to how well they satisfy it. Diierent ways of deening the relation in terms of the default information can be used to give diierent granularities to the default status the information h...