Nikita Taneja

Work place: MRU/CSE/Faridabad, Haryana, 121004



Research Interests: Artificial Intelligence, Computational Learning Theory, Information Systems, Data Mining, Information Retrieval, Information Storage Systems, Multimedia Information System, Data Compression


Ms. Nikita Taneja is currently working as a Research Professional at Siemens Technology and Services Private Limited. She has more than 13 years of teaching and industry experience. She is pursuing her Ph.D. (Computer Science and Engineering) from Manav Rachna University in the field of Cross Domain Recommender systems. Her areas of interest include Information Retrieval, Machine Learning, Artificial Intelligence, Data Mining.

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

By Nikita Taneja Hardeo Kumar Thakur

DOI:, 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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