Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Abstract

Commodity recommendation plays an essential role in the marketing field in the Internet era, and collaborative filtering, as a powerful technique of commodity recommendation, has been widely concerned in both academic studies and practical applications. Existing research on collaborative filtering often uses methods such as genetic algorithm and neural network to solve the sparsity and cold start problems while ignoring the fuzziness of users' ratings on goods or services. To solve the problems, we propose a recommendation algorithm (IFR-CF) based on intuitionistic fuzzy reasoning and collaborative filtering. In this algorithm, the characteristic coefficient in intuitionistic fuzzy reasoning is used to replace the traditional similarity coefficient to determine neighbor set, and the finite prior ordering method is used to replace traditional algorithm to recommend commodity. Two groups of data are extracted from Movielens and Jester datasets for experiments, and the MAE value generated by the recommended items is taken as the metric to verify the algorithm performance. Experimental results show that compared with the traditional algorithms, our algorithm achieves lower MAE value and higher recommendation accuracy. Meanwhile, the intuitionistic index of fuzzy set is taken into account in the calculation of the hesitation coefficient, which provides a novel solution to the problem of missing scoring data of users.