Artificial Intelligence


FNHSM_HRS: Hybrid Recommender System Using Fuzzy Clustering and Heuristic Similarity Measure

Authors: Mostafa Khalaji, Chitra Dadkhah

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.

Comments: 6 Pages. Conference: 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, 18th Conference on Fuzzy Systems and 17th Conference on Intelligent Systems, Bojnord, Iran, University of Bojnord, p.p. 562-568, January 2019. Persian formatted

Download: PDF

Submission history

[v1] 2019-09-29 14:36:17

Unique-IP document downloads: 9 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus