TY - GEN
T1 - Mining web log data for personalized recommendation system
AU - Rosyidah, Asma
AU - Prajitno, Isti Surjandari
AU - Zulkarnain, null
N1 - Funding Information:
The authors would like to express gratitude and appreciation to Universitas Indonesia for financing this study through PITTA Research Grants Universitas Indonesia, Nomor: 2455/UN2.R3.1/HKP.05.00/2018.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/8
Y1 - 2018/11/8
N2 - Increase in number of internet users in Indonesia boosts the development of e-commerce platform. Whereas potential access to a larger and more diverse customer base is generally viewed as an opportunity, it can also represent increase in competition among e-commerce platforms. Hence, e-commerce needs to develop sophisticated strategies to attract and retain customers, one of which is done through personalization in web services. Recommendation system, one form of web service personalization in e-commerce platform, predicts user preferences and helps them find products that they may be interested in by implementing web mining techniques. This empirical research investigated user web log data which illustrate behavior and implicit preferences of customers in one of ecommerce in Indonesia to predict user preferred product category in their future request. In this study, model-based recommendation system was built based on users' activity in a session and site type using C5.0 algorithm of decision tree technique. Top N recommendations were given based on probability-based ranking of categories resulted from probability estimation of the decision tree.
AB - Increase in number of internet users in Indonesia boosts the development of e-commerce platform. Whereas potential access to a larger and more diverse customer base is generally viewed as an opportunity, it can also represent increase in competition among e-commerce platforms. Hence, e-commerce needs to develop sophisticated strategies to attract and retain customers, one of which is done through personalization in web services. Recommendation system, one form of web service personalization in e-commerce platform, predicts user preferences and helps them find products that they may be interested in by implementing web mining techniques. This empirical research investigated user web log data which illustrate behavior and implicit preferences of customers in one of ecommerce in Indonesia to predict user preferred product category in their future request. In this study, model-based recommendation system was built based on users' activity in a session and site type using C5.0 algorithm of decision tree technique. Top N recommendations were given based on probability-based ranking of categories resulted from probability estimation of the decision tree.
KW - Decision tree C5.0
KW - E-commerce
KW - Personalization
KW - Recommendation system
KW - Web log
KW - Web mining
UR - http://www.scopus.com/inward/record.url?scp=85058412940&partnerID=8YFLogxK
U2 - 10.1109/ICoICT.2018.8528799
DO - 10.1109/ICoICT.2018.8528799
M3 - Conference contribution
AN - SCOPUS:85058412940
T3 - 2018 6th International Conference on Information and Communication Technology, ICoICT 2018
SP - 441
EP - 446
BT - 2018 6th International Conference on Information and Communication Technology, ICoICT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information and Communication Technology, ICoICT 2018
Y2 - 3 May 2018 through 4 May 2018
ER -