Abstract
This research work is on optimizing the number of query parameters required to recommend an e-learning platform. This paper proposes a new methodology for efficient implementation by forming lattice on query parameters. This lattice structure helps to co-relate the different query parameters that in turn form association rules among them. The proposed methodology is conceptualized on an e-learning platform with the objective of formulating an effective recommendation system to determine associations between various products offered by the e-learning platform by analyzing the minimum set of query parameters.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ricci, F., & Rokach, L., Shapira, B. (2011). Recommender systems handbook. Springer, Berlin.
Jansen, B. J. (2009). Understanding user-web interactions via web analytics. In Synthesis Lectures on Information Concepts, Retrieval, and Services.
G. Zheng, S. Peltsverger. (2015) “Web Analytics Overview”, in book, “Encyclopedia of Information Science and Technology”, 3rd Edition, IGI Global, 2015.
Morales, C. R., Ventura, S. (2005). “Data Mining in E-Learning” WIT Transactions on State-of-the-art in Science and Engineering Book Series 4, Transaction Vol. 4.
Raghavan, S. (2005). Data mining in e-commerce: A survey. N.R. Sadhana, 30(2–3).
Hu. J. (2010). Data mining and e-commerce, study conducted for eBay.
Vyas, M. S., & Gulwani, R. (2017). Predictive analytics for E learning system. In International Conference on Inventive Systems and Control (ICISC).
Moubayed, A., Injadat, M., Nassif, A. B., Lutfiyya, H., & Shami, A. (2018). E-learning: Challenges and research opportunities using machine learning & data analytics, (in English). IEEE Access, 6, 39117–39138.
Li, X., Zhang, X., Fu, W., & Liu, X. (2015). E-Learning with visual analytics. In IEEE Conference on e-Learning, e-Management and e-Services (IC3e).
Zhou, D., Li, H., Liu, S., Song, B., & Hu T. X. (2017). A map-based visual analysis method for patterns discovery of mobile learning in education with big data. In IEEE International Conference on Big Data.
Premchaiswadi, W., Porouhan, P., & Premchaiswadi, N. (2018). Process modeling, behavior analytics and group performance assessment of e-learning logs via fuzzy miner algorithm. In 42nd Annual Computer Software and Applications Conference (COMPSAC).
Sen, S., Chaki, N., & Cortessi, A. (2009). Optimal space and time complexity analysis on the lattice of cuboids using galois connections for data warehousing. In. 4th International Conference on Computer Sciences and Convergence Information Technology (ICCIT).
Sen, S., Roy, S., Sarkar, A., Chaki, N., & Debnath, N. C. (2014). Dynamic discovery of query path on the lattice of cuboids using hierarchical data granularity and storage hierarchy. Elsevier Journal of Computational Science, 5(4).
Roy, S., Sen, S., & Debnath, N. C. (2018). Optimal query path selection in lattice of cuboids using novel heuristic search algorithm. In 33rd International Conference on Computers and their Applications (CATA).
Ding, Q., Ding, Q., & Perrizo, W. (2008). PARM—an efficient algorithm to mine association rules from spatial data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(6).
Chapman, C., & Feit, E. M. (2019). Association rules for market basket analysis. In R For Marketing Research and Analytics. Use R!. Springer, Cham.
Faridizadeh, S., Abdolvand, N., Harandi, S., & Rajaee, S. (2018). Market basket analysis using community detection approach: A real case. In M. Moshirpour, B. Far, & R. Alhajj (Eds.), Applications of data management and analysis., Lecture notes in social networks Cham: Springer.
Maji, G., Sen, S., & Sarkar, A. (2017). Share market sectorial Indices movement forecast with lagged correlation and association rule mining. 16th International Conference on Computer Information Systems and Industrial Management Applications (CISIM).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghosh, S., Roy, S., Sen, S. (2021). An Efficient Recommendation System on E-Learning Platform by Query Lattice Optimization. In: Sharma, N., Chakrabarti, A., Balas, V., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1174. Springer, Singapore. https://doi.org/10.1007/978-981-15-5616-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-15-5616-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5615-9
Online ISBN: 978-981-15-5616-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)