Sandeep Kumar

Work place: Department of Computer Science & Engineering, Sharda School of Engineering & Technology, Sharda University, Greater Noida, 201310, India



Research Interests: Data Mining, Artificial Intelligence


Dr. Sandeep Kumar is an associate professor in the Department of Computer Science Engineering, School of Engineering and Technology, Sharda University. He holds a Ph.D. Degree in Computer Science. His areas of research include Data Mining, Fractal Graphics, and Artificial Intelligence.

Author Articles
An Enhanced Approach to Recommend Data Structures and Algorithms Problems Using Content-based Filtering

By Aayush Juyal Nandini Sharma Pisati Rithya Sandeep Kumar

DOI:, Pub. Date: 8 Oct. 2023

Data Structures and Algorithms (DSA) is a widely explored domain in the world of computer science. With it being a crucial topic during an interview for a software engineer, it is a topic not to take lightly. There are various platforms available to understand a particular DSA, several programming problems, and its implementation. Hacckerank, LeetCode, GeeksForGeeks (GFG), and Codeforces are popular platforms that offer a vast collection of programming problems to enhance skills. However, with the huge content of DSA available, it is challenging for users to identify which one among all to focus on after going through the required domain. This work aims to use a Content-based filtering (CBF) recommendation engine to suggest users programming-based questions related to different DSAs such as arrays, linked lists, trees, graphs, etc. The recommendations are generated using the concept of Natural Language Processing (NLP). The data set consists of approximately 500 problems. Each problem is represented by the features such as problem statement, related topics, level of difficulty, and platform link. Standard measures like cosine similarity, accuracy, precision, and F1-score are used to determine the proportion of correctly recommended problems. The percentages indicate how well the system performed regarding that evaluation. The result shows that CBF achieves an accuracy of 83 %, a precision of 83 %, a recall of 80%, and an F1-score of 80%. This recommendation system is deployed on a web application that provides a suitable user interface allowing the user to interact with other features. With this, a whole E-learning application is built to aid potential software engineers and computer science students. In the future, two more recommendation systems, Collaborative Filtering (CF) and Hybrid systems, can be implemented to make a comparison and decide which is most suitable for the given problem statement.

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