Next Article in Journal
Three-Factor Fast Authentication Scheme with Time Bound and User Anonymity for Multi-Server E-Health Systems in 5G-Based Wireless Sensor Networks
Next Article in Special Issue
Recognition of the Driving Style in Vehicle Drivers
Previous Article in Journal
Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry
Previous Article in Special Issue
A New Model for Predicting Rate of Penetration Using an Artificial Neural Network
Open AccessArticle

Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System

1
Department of Computer Engineering, Chung-Ang University, 84 Heukseok, Seoul 156-756, Korea
2
Big Data Research Group, Western Norway Research Institute, Box 163, NO-6851 Sogndal, Norway
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2510; https://doi.org/10.3390/s20092510
Received: 21 March 2020 / Revised: 25 April 2020 / Accepted: 26 April 2020 / Published: 29 April 2020
The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity. View Full-Text
Keywords: cross-domain; user rating consolidation; recommendation system; inner approximation; implicit update; convex optimization cross-domain; user rating consolidation; recommendation system; inner approximation; implicit update; convex optimization
Show Figures

Figure 1

MDPI and ACS Style

Vo, N.D.; Hong, M.; Jung, J.J. Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System. Sensors 2020, 20, 2510.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop