A Knowledge Based Recommender System with Multigranular Linguistic Information

Recommender systems are applications that have emerged in the e-commerce area in order to assist users in their searches in electronic shops. These shops usually offer a wide range of items that cover the necessities of a great variety of users. Nevertheless, searching in such a wide range of items could be a very difficult and time-consuming task. Recommender systems assist users to find out suitable items by means of recommendations based on information provided by different sources such as: other users, experts, item features, etc. Most of the recommender systems force users to provide their preferences or necessities using an unique numerical scale of information fixed in advance. In spite of this information is usually related to opinions, tastes and perceptions, therefore, it seems that is usually better expressed in a qualitative way, with linguistic terms, than in a quantitative way, with precise numbers. We propose a Knowledge Based Recommender System that uses the fuzzy linguistic approach to define a flexible framework to capture the uncertainty of the user’s preferences. Thus, this framework will allow users to express their necessities in scales closer to their own knowledge, and different from the scale utilized to describe the items.