Recommendation System Using Autoencoders
Source abstract
Abstract
The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Image Retrieval and Classification Techniques · Advanced Data Compression Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Algoritmi Research Center, University of Minho, Campus of Gualtar, 4710 Braga, Portugal
Department of Informatics, University of Minho, Campus of Gualtar, 4710 Braga, Portugal
References (52)
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