October 9, 2014
Publications
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Scientific Journal
No

Smart Innovation, Systems and Technologies

Publicated to: - (), DOI: 21903018

Authors: BRISO RODRIGUEZ, CESAR

Affiliations

Universidad Politécnica de Madrid - Author

Abstract

Neural Collaborative Filtering recommendations are traditionally based on regression architectures (returning continuous predictions, e.g. 2.8 stars), such as DeepMF and NCF. However, there are advantages in the use of collaborative filtering classification models. This work tested both neuronal approaches using a set of representative open datasets, baselines, and quality measures. The results show the superiority of the regular regression model compared to the regular classification model (returning discrete predictions, e.g. 1–5 stars) and the binary classification model (returning binary predictions: recommended, non-recommended). Results also show a similar performance when comparing our proposed recommendation neural approach with the state-of-the-art neural regression baseline. The key issue is the additional information the recommendation approach provides compared to the regression model: While the regression baseline only returns the recommendation values, the proposed recommendation model returns ⟨ value, probability ⟩ pairs. Extra probability information can be used in the recommender systems area for different objectives: recommendation explanation, visualization of results, quality improvements, mitigate attack risks, etc. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Binary classificationClassification modelClassification modelsCollaborative filteringCollaborative filtering recommendationsDeep learningForecastingNeural collaborative filteringPerformanceQuality measuresRecommender systemsRegression analysisRegression modellingRegular regressionStarsState of the art

Quality index

Impact and social visibility

Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 17 - Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development, with a probability of 48% according to the mBERT algorithm developed by Aurora University.

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Germany; United States of America.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: Last Author ().