Hardeo Kumar Thakur

Work place: COE Division, NSIT, University of Delhi, India

E-mail: hardeokumar@gmail.com

Website: https://orcid.org/0000-0002-2954-1308

Research Interests: Data Mining


Hardeo Kumar Thakur is a Teaching cum research Fellow in Netaji Subhas Institute of Technology (NSIT). NSIT is affiliated to Delhi University, India. He is pursuing his Ph.D. in the area of dynamic graph mining.

Author Articles
Evaluating the Scalability of Matrix Factorization and Neighborhood Based Recommender Systems

By Nikita Taneja Hardeo Kumar Thakur

DOI: https://doi.org/10.5815/ijitcs.2023.01.03, Pub. Date: 8 Feb. 2023

Recommendation Systems are everywhere, from offline shopping malls to major e-commerce websites, all use recommendation systems to enhance customer experience and grow profit. With a growing customer base, the requirement to store their interest, behavior and respond accordingly requires plenty of scalability. Thus, it is very important for companies to select a scalable recommender system, which can provide the recommendations not just accurately but with low latency as well. This paper focuses on the comparison between the four methods KMeans, KNN, SVD, and SVD++ to find out the better algorithm in terms of scalability. We have analyzed the methods on different parameters i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Precision, Recall and Running Time (Scalability). Results are elaborated such that selection becomes quite easy depending upon the user requirements.

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Mining Maximal Quasi Regular Patterns in Weighted Dynamic Networks

By Hardeo Kumar Thakur Anand Gupta Bhavuk Jain Ambika

DOI: https://doi.org/10.5815/ijitcs.2017.04.07, Pub. Date: 8 Apr. 2017

Interactions appearing regularly in a network may be disturbed due to the presence of noise or random occurrence of events at some timestamps. Ignoring them may devoid us from having better understanding of the networks under consideration. Therefore, to solve this problem, researchers have attempted to find quasi/quasi-regular patterns in non-weighted dynamic networks. To the best of our knowledge, no work has been reported in mining such patterns in weighted dynamic networks. So, in this paper we present a novel method which mines maximal quasi regular patterns on structure (MQRPS) and maximal quasi regular patterns on weight (MQRPW) in weighted dynamic networks. Also, we have provided a relationship between MQRPW and MQRPS which facilitates in the running of the proposed method only once, even when both are required and thus leading to reduction in computation time. Further, the analysis of the patterns so obtained is done to gain a better insight into their nature using four parameters, viz. modularity, cliques, most commonly used centrality measures and intersection. Experiments on Enron-email and a synthetic dataset show that the proposed method with relationship and analysis is potentially useful to extract previously unknown vital information.

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