HU-FCF: A hybrid user-based fuzzy collaborative filtering method in Recommender Systems

• We proposed a systematic mathematical definition of Fuzzy Recommender Systems.
• Some algebraic operations accompanied with their properties were described.
• We proposed a novel hybrid user-based fuzzy collaborative filtering method.
• This algorithm obtained better accuracy than the relevant fuzzy filtering methods.
• An application for the football results prediction was illustrated.

Recommender Systems (RS) have been being captured a great attraction of researchers by their applications in various interdisciplinary fields. Fuzzy Recommender Systems (FRS) is an extension of RS with the fuzzy similarity being calculated based on the users’ demographic data instead of the hard user-based degree. Based upon the observations that the FRS researches did not offer a mathematical definition of FRS accompanied with its algebraic operations and properties, and the fuzzy similarity degree is not enough to express accurately the analogousness between users, in this paper we will present a systematic mathematical definition of FRS including theoretical analyses of algebraic operations and properties and propose a novel hybrid user-based fuzzy collaborative filtering method that integrates the fuzzy similarity degrees between users based on the demographic data with the hard user-based degrees calculated from the rating histories into the final similarity degrees in order to obtain high accuracy of prediction. Experimental results on some benchmark datasets show that the proposed method obtains better accuracy than other relevant methods. Lastly, an application for the football results prediction is given to illustrate the uses of the proposed method.