Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation

The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize the historical ratings embedded in the recommendation system and studies the data processing problem of the sparse matrix under the condition of a hesitant fuzzy set. The key is to transform the similarity problem of products in the sparse matrix into the similarity problem of two hesitant fuzzy sets by data conversion, data processing, and data complement. This paper further considers the influence of the difference of user ratings on the recommendation results and obtains a user's recommendation list. On the one hand, the proposed method effectively solves the matrix in the recommendation system; on the other hand, it provides a feasible method for calculating similarity in the recommendation system.

Automated summary

This paper investigates the sparse matrix processing issue in recommendation system using hesitant fuzzy set method. By transforming and processing data, it effectively solves the product similarity problem in the sparse matrix, offering a feasible approach for calculating similarity in the recommendation system.