Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Explainable machine learning in image classification models: An uncertainty quantification perspective
The poor explainability of deep learning models has hindered their adoption in safety and
quality-critical applications. This paper focuses on image classification models and aims to …
quality-critical applications. This paper focuses on image classification models and aims to …
An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information
In recent years, explainable recommendation has been a topic of active study. This is
because the branch of the machine learning field related to methodologies is enabling …
because the branch of the machine learning field related to methodologies is enabling …
Training Robust Deep Collaborative Filtering Models via Adversarial Noise Propagation
The recommendation performance of deep collaborative filtering models drops sharply
under imperceptible adversarial perturbations. Some methods promote the robustness of …
under imperceptible adversarial perturbations. Some methods promote the robustness of …
Attribute-based neural collaborative filtering
The core task of recommendation systems is to capture user preferences for items. Dot
product operations are usually used to mine user preferences for items. However, the dot …
product operations are usually used to mine user preferences for items. However, the dot …
Taylor-ChOA: Taylor-chimp optimized random multimodal deep learning-based sentiment classification model for course recommendation
Course recommendation is a key for achievement in a student's academic path. However, it
is challenging to appropriately select course content among numerous online education …
is challenging to appropriately select course content among numerous online education …
OnML: an ontology-based approach for interpretable machine learning
In this paper, we introduce a novel interpreting framework that learns an interpretable model
based on an ontology-based sampling technique to explain agnostic prediction models …
based on an ontology-based sampling technique to explain agnostic prediction models …
Enhancing mobile app recommendations through adaptive fusion of long-term stability and short-term interests
The exponential growth in mobile application has greatly enhanced convenience in daily
life, yet it has also complicated the process for users to find necessary apps in crowded …
life, yet it has also complicated the process for users to find necessary apps in crowded …
Cognitive process-driven model design: A deep learning recommendation model with textual review and context
Online reviews play a crucial role in comprehending user rating behavior and improving
personalized recommendations in e-commerce. However, existing review-based …
personalized recommendations in e-commerce. However, existing review-based …
SMAR: Summary-Aware Multi-Aspect Recommendation
Extracting user preferences and item features from reviews to assist recommendations is
becoming increasingly popular. However, on the one hand, existing works generally select …
becoming increasingly popular. However, on the one hand, existing works generally select …