Journal of Information Organization


Vol No. 11 ,Issue No. 3 2021

A Model for the Recommender System Creation and Collaborative System Filtering Using Data Management
Katarina Jovanovic and Milena Stankovic
Faculty of Electronic Engineering at University of Niš Aleksandra Medvedeva 14, Niš 1800 Serbia
Abstract: In recommender system, data management techniques are important. Using collaborative filtering and zero suppressed binary decision diagram, we have developed a model for the recommender system creation. We have used the diagrams of the binary decision diagrams to analyse the issues of the large data sets. To prepare the user product matrix for preserving the user behaviour of the collaborative filtering system, binary decision diagrams are required. We in this work have developed recommendation system for a class of users which provide the requirements for each user based on the diagram traversal. We found that when large number of users and products are available, this method of the recommender system will be useful.
Keywords: Collaborative Filtering, Zero-suppressed BDD, Tuple Histogram, Pattern Histogram A Model for the Recommender System Creation and Collaborative System Filtering Using Data Management
DOI:https://doi.org/10.6025/jio/2021/11/3/72-77
Full_Text   PDF 547 KB   Download:   220  times
References:

[1] Asanov, D. Algorithms and Methods in Recommender System, Berlin Institute of Technology, Berlin, Germany.
[2] Ekstran, M. D., Riedl, J. T., Konstan, J. A. (2011). Collaborative Filtering Recommender Systems, Foundations and Trends® in Human–Computer Interaction: 4 (2) 81–173.
[3] Jones, M. (2017). Recommender systems, http://www.ibm.com/developerworks/library/osrecommender1/, accessed on 01.05.2017.
[4] Lee., J., Sun, M., Lebanon, G. (2012). A Comparative Study of Collaborative Filtering Algorithm”, Workshops, Demos, and ArXiv Preprints.
[5] Melville, P., Sindhwani, V. (2010). Recommender system, In: C. Sammut and G. Webb, Eds., Encyclopedia of Machine Learning, Springer, Berlin, 2010, 829-838.
[6] Minato, S., Arimura, H. (2006). Generating Frequent Closed Item Sets Based on Zero-suppressed BDDs, Hokkaido University, Division of Computer Science, TCS Technical Reports, TCSTR- A-06-17, July.
[7] Minato, S., Arimura, H. (2004). Combinatorial Item Set Analysis Based on Zero-Suppressed BDDs, Hokkaido University, Division of Computer Science, TCS Technical Reports, TCSTR- A-04-1, Dec, 2004.
[8] Su, X., Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, vol 2009, Article ID 421425, 19 pages.
[9] Sarwar, B., Karypis, G., Konstan, J., Reidl, J. Item-based collaborative filtering recommendation algorithm, In: Proceedings of the 10th International Conference on World Wide Web (285-295). (WWW ’01). New York, NY, USA: ACM.