Multi‐model deep learning approach for collaborative filtering recommendation system

Abstract

As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback. Though implicit feedback is too challenging, it is highly applicable to use in building recommendation systems. Conventional collaborative filtering techniques such as matrix decomposition, which consider user preferences as a linear combination of user and item latent features, have limited learning capacities, hence suffer from a cold start and data sparsity problems. To tackle these problems, the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features. Conversely, the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations. Therefore, the authors proposed a multi‐model deep learning (MMDL) approach by integrating user and item functions to construct a hybrid RS and significant improvement. The MMDL approach combines deep autoencoder with a one‐dimensional convolution neural network model that learns user and item features to predict user preferences. From detail experimentation on two real‐world datasets, the proposed work exhibits substantial performance when compared to the existing methods.

A Hybrid Newton–Raphson and Particle Swarm Optimization Method for Target Motion Analysis by Batch Processing

“…The hybrid optimization algorithm proposed in this paper, i.e., Newton–Raphson particle swarm optimization (NRPSO), combines the advantages of NRM and PSO in BO-TMA. Interest in hybrid optimization methods has increased over the past few decades [ 14 , 15 , 16 , 17 , 22 , 23 , 24 ]. Early hybridization was mainly done between several metaheuristic algorithms.…”

“…In the field of electrical impedance tomography, the quality of the reconstructed image was improved by optimizing the radial basis function neural network (RBFNN) with the hybrid particle swarm optimization (HPSO) algorithm [ 16 ]. A multimodel deep learning (MMDL) framework has been proposed that takes into account the strengths of a deep autoencoder neural network (DeepAEC) and one-dimensional conventional neural network (1D-CNN) to effectively enhance the performance of the recommender system (RS) [ 17 ]. However, BO-TMA that uses a hybrid method combining deterministic and heuristic methods has not yet been reported.…”

Research on Online Learner Modeling and Course Recommendation Based on Emotional Factors

“…e hybrid model integrates RNN and LSTM [7] and provides better results than existing models in terms of accuracy. Literature [8] uses the performance of big data artificial intelligence platform for qualitative purposes and shows the practical literature as the future algorithm experiment and system joint design benchmark. Literature [9] aims at the traditional collaborative filtering technology, such as matrix decomposition.…”

An efficient hybrid recommendation model based on collaborative filtering recommender systems

“…The model-based CF model is scalable and can handle higher sparsity than the memory-based method, but when users or items without any ratings enter the system it is also miserable. Different machine learning algorithms are based on the model-based CF method such as the matrix factorization algorithm [17], neural networks [5], Bayesian classifiers [18], the clustering algorithm [19], genetic algorithms [20], and the regression model [21] among others.…”