Open AccessAn Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation
Author(s) -
Youcef Djenouri,
Asma Belhadi,
Gautam Srivastava,
Jerry ChunWei Lin
Publication year - 2023
Publication title -
acm transactions on multimedia computing, communications and applications/acm transactions on multimedia computing communications and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.558
H-Index - 49
eISSN - 1551-6865
pISSN - 1551-6857
DOI - 10.1145/3524022
Subject(s) - computer science , convolutional neural network , deep learning , process (computing) , artificial intelligence , set (abstract data type) , machine learning , convolution (computer science) , data set , artificial neural network , speedup , baseline (sea) , data mining , parallel computing , oceanography , programming language , geology , operating system
This paper proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets orpheline tweet: It is a tweet with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI (XAI) is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of\(90\% \) when analyzing complex Wikipedia data while the other algorithms did not achieve\(85\% \) when processing the same amount of data.