TY - GEN
T1 - A Novel Recommender System Based on Apriori Algorithm for Requirements Engineering
AU - Alzu'Bi, Shadi
AU - Hawashin, Bilal
AU - Eibes, Mohammad
AU - Al-Ayyoub, Mahmoud
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/30
Y1 - 2018/11/30
N2 - Even though requirement gathering is an important step in the construction of any project, it imposes tedious work on the side of the system administrator. Recently, with the advent of data mining methods, many opportunities for improvements on the requirement gathering process have become available, one of the which is the use of recommender systems. Recommendation systems for requirements engineering can be used to provide the right information at the right time to requirements engineers. In this work, we propose a novel efficient recommender system based on Apriori algorithm for user requirements. Such recommender system would improve the accuracy of the obtained requirements and produce more comprehensive results. Furthermore, it would provide interesting information that can be used by various parties. Experimental work showed that our recommender system is efficient in term of execution time and can be widely implemented. In details, the system needed 11-21 seconds to execute when the number of users was 2000-4000.
AB - Even though requirement gathering is an important step in the construction of any project, it imposes tedious work on the side of the system administrator. Recently, with the advent of data mining methods, many opportunities for improvements on the requirement gathering process have become available, one of the which is the use of recommender systems. Recommendation systems for requirements engineering can be used to provide the right information at the right time to requirements engineers. In this work, we propose a novel efficient recommender system based on Apriori algorithm for user requirements. Such recommender system would improve the accuracy of the obtained requirements and produce more comprehensive results. Furthermore, it would provide interesting information that can be used by various parties. Experimental work showed that our recommender system is efficient in term of execution time and can be widely implemented. In details, the system needed 11-21 seconds to execute when the number of users was 2000-4000.
UR - https://www.scopus.com/pages/publications/85060016606
U2 - 10.1109/SNAMS.2018.8554909
DO - 10.1109/SNAMS.2018.8554909
M3 - Conference contribution
AN - SCOPUS:85060016606
T3 - 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018
SP - 323
EP - 327
BT - 2018 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Social Networks Analysis, Management and Security, SNAMS 2018
Y2 - 15 October 2018 through 18 October 2018
ER -