Sampling and noise filtering methods for recommender systems: A literature review
In the era of online business, many e-commerce sites have evolved which recommend items
according to one's needs and interests. Plenty of data is available to be processed to make …
according to one's needs and interests. Plenty of data is available to be processed to make …
[HTML][HTML] Survival of e-commerce entrepreneurs: The importance of brick-and-click and internationalization strategies
B Cuellar-Fernández, Y Fuertes-Callén… - … commerce research and …, 2021 - Elsevier
E-commerce is a fast-growing industry that attracts many entrepreneurs; however, the
survival rate is lower than that of other industries. Entrepreneurs take many strategic …
survival rate is lower than that of other industries. Entrepreneurs take many strategic …
Hybrid gated recurrent unit and convolutional neural network-based deep learning mechanism for efficient shilling attack detection in social networks
The degree of openness of the socially aware recommendation systems and the possibility
of the attackers injecting vast numbers of fake profiles biases the prediction of the system …
of the attackers injecting vast numbers of fake profiles biases the prediction of the system …
A genre trust model for defending shilling attacks in recommender systems
L Yang, X Niu - Complex & Intelligent Systems, 2023 - Springer
Shilling attacks have been a significant vulnerability of collaborative filtering (CF)
recommender systems, and trust in CF recommender algorithms has been proven to be …
recommender systems, and trust in CF recommender algorithms has been proven to be …
Shilling Attack Detection with One Class Support Vector Machines
Recommender systems play a vital role in various online platforms, assisting users in
discovering new products, services, and content considering their preferences. However …
discovering new products, services, and content considering their preferences. However …
Mitigating Human and Computer Opinion Fraud via Contrastive Learning
Y Tukmacheva, I Oseledets, E Frolov - arXiv preprint arXiv:2301.03025, 2023 - arxiv.org
We introduce the novel approach towards fake text reviews detection in collaborative
filtering recommender systems. The existing algorithms concentrate on detecting the fake …
filtering recommender systems. The existing algorithms concentrate on detecting the fake …
Enhancing popSAD: A New Approach to Shilling Attack Detection in Collaborative Recommenders
Maintaining the integrity and reliability of user-generated content requires the detection of
shilling profiles or attack profiles in recommender systems. By embracing the concepts of …
shilling profiles or attack profiles in recommender systems. By embracing the concepts of …
Push and nuke attacks detection using DNN-HHO algorithm
Collaborative recommender systems are widely used as a tool to offer recommendation for a
product to its users. These systems produce recommendations to its users using information …
product to its users. These systems produce recommendations to its users using information …
Logical Study of Predictions and Gathering Methodologies to Enhance Co-Clustering Formulation from a Period of Change Information in Machine Learning
These days, AI is assuming an imperative function to remove and distinguish the helpful
highlights that best speak to information in different fields, containing natural information …
highlights that best speak to information in different fields, containing natural information …
Analytical study of predictions and clustering methodologies to enhance co-clustering formulation from time-changing information in machine learning
JN Kumar, M Sheeba, M Swathi - AIP Conference Proceedings, 2022 - pubs.aip.org
Nowadays, machine learning is playing vital role to extract and identify the useful features
that best represent data in various fields, containing biological data analysis, content mining …
that best represent data in various fields, containing biological data analysis, content mining …