Open Access

ARTICLE

Deep Embedded Fuzzy Clustering Model for Collaborative Filtering Recommender System

Adel Binbusayyis^*

College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, AL Kharj, Saudi Arabia

* Corresponding Author: Adel Binbusayyis. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 501-513. https://doi.org/10.32604/iasc.2022.022239

Received 01 August 2021; Accepted 11 October 2021; Issue published 05 January 2022

Abstract

The increasing user of Internet has witnessed a continued exploration in applications and services that can bring more convenience in people's life than ever before. At the same time, with the exploration of online services, the people face unprecedented difficulty in selecting the most relevant service on the fly. In this context, the need for recommendation system is of paramount importance especially in helping the users to improve their experience with best value-added service. But, most of the traditional techniques including collaborative filtering (CF) which is one of the most successful recommendation technique suffer from two inherent issues namely, rating sparsity and cold-start. Inspired by the breakthrough results and great strides of deep learning in a wide variety of applications, this work for the first time proposes a deep clustering model leveraging the strength of deep learning and fuzzy clustering for user-based collaborative filtering (UCF). At first, the proposed model applies deep autoencoder (AE) to learn users’ latent feature representation from the original rating matrix and then conducts fuzzy clustering assignments on learnt users’ latent representation to form user clusters. More importantly, the proposed model defines a unified objective function combining the reconstruction loss and clustering loss to yield end-to-end UCF process that can jointly optimize the process of data representation learning and clustering assignment. The effectiveness of the proposed model is evaluated with two real-world datasets, MoiveLens100K and MovieLens1M using two standard performance metrics. Experimental results of the proposed model demonstrated the prospects for gain in top-K recommendation accuracy against recent related works with resistant to rating sparsity and cold start problems.

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Keywords

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