VLDB 2026 Research / reviewers in the wild / expert

Veer Sain Dixit

dblp:125/1524 · DBLP profile ↗
← Back
8ranked-venue papers
5as first author
2since 2021 · last 2023
0000-0003-1092-8657ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 6 · 4 first-authorSecurity and privacy · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 1
YearPublicationVenuePosition
2023 Push and nuke attacks detection using DNN-HHO algorithm
Veer Sain Dixit, Akanksha Bansal Chopra
 Int. J. Inf. Comput. Secur.1
2023 Collaborative filtering-based recommendations against shilling attacks with particle swarm optimiser and entropy-based mean clustering
Anjani Kumar Verma, Veer Sain Dixit
 Int. J. Inf. Comput. Secur.2
2018 Recommendations with Sparsity Based Weighted Context Framework
Veer Sain Dixit, Parul Jain
 ICCSA (4)1
2018 A Business Intelligent Framework to Evaluate Prediction Accuracy for E-Commerce Recommenders
Shalini Gupta, Veer Sain Dixit
 ICCSA (4)2
2014 Weighted-Frequent Itemset Refinement Methodology (W-FIRM) of Usage Clusters
Veer Sain Dixit, Shveta Kundra Bhatia, Sarabjeet Kaur
 ICCSA (5)1
2014 Evaluation of Web Session Cluster Quality Based on Access-Time Dissimilarity and Evolutionary Algorithms
Veer Sain Dixit, Shveta Kundra Bhatia, V. B. Singh
 ICCSA (5)1
2013 Cross Project Validation for Refined Clusters Using Machine Learning Techniques
Veer Sain Dixit, Shveta Kundra Bhatia
 ICCSA (2)1
2012 Refinement of recommendations based on user preferences
abstract
Collaborative Filtering is one of the most researched techniques. It generates recommendations from similar taste users in a group. In this paper, Information Theoretic Techniques are used to propose an Online Recommendation Generator based on Collaborative Filtering. It initially generates preliminary recommendations based on positive and negative user preferences and further refines these preliminary recommendations based on opposite user preferences. Experiments are conducted using MovieLens Dataset and considerable improvement in accuracy is seen in the results.
Harita Mehta, Veer Sain Dixit, Punam Bedi
 ISDA2