FNHSM_HRS: Hybrid recommender system using fuzzy clustering and heuristic similarity measure

TL;DR

This paper introduces FNHSM_HRS, a hybrid recommender system combining fuzzy clustering and a new heuristic similarity measure to improve scalability and accuracy in collaborative filtering, especially with sparse data.

Contribution

The paper presents a novel hybrid recommender system that integrates fuzzy clustering with a new heuristic similarity measure to enhance performance and scalability.

Findings

Improved accuracy in recommendations on MovieLens dataset.

Enhanced scalability handling large user-item datasets.

Better performance metrics compared to traditional similarity measures.

Abstract

Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of users. One of the simplest and most comprehensible and successful models is to find users with a taste in recommender systems. In this model, with increasing number of users and items, the system is faced to scalability problem. On the other hand, improving system performance when there is little information available from ratings, that is important. In this paper, a hybrid recommender system called FNHSM_HRS which is based on the new heuristic similarity measure (NHSM) along with a fuzzy clustering is presented. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system recommendations. The proposed system is based on the collaborative filtering model and is partnered with the heuristic similarity measure to improve the system's performance and accuracy. The evaluation of the proposed system based results on the MovieLens dataset carried out the results using MAE, Recall, Precision and Accuracy measures Indicating improvement in system performance and increasing the accuracy of recommendation to collaborative filtering methods which use other measures to find similarities.