NDLI: Recommendation from access logs with ensemble learning

Content Provider	Springer Nature Link
Author	Ayaki, Takashi Yanagimoto, Hidekazu Yoshioka, Michifumi
Copyright Year	2017
Abstract	Many recommendation systems find similar users based on a profile of a target user and recommend products that he/she may be interested in. The profile is constructed with his/her purchase histories. However, histories of new customers are not stored and it is difficult to recommend products to them in the same fashion. The problem is called a cold start problem. We propose a recommendation method using access logs instead of purchase histories, because the access logs are gathered more easily than purchase histories and the access logs include much information on their interests. In this study, we construct user’s profiles using product categories browsed by them from their access logs and predict products with Gradient Boosting Decision Tree. In addition, we carry out evaluation experiments using access logs in a real online shop and discuss performance of our proposed method comparing with conventional machine learning and Support Vector Machine (SVM). We confirmed that the proposed method achieved higher precision than SVM over 10 data sets. Especially, under unbalanced data sets, the proposed method is superior to SVM.
Starting Page	163
Ending Page	167
Page Count	5
File Format	PDF
ISSN	14335298
Journal	Artificial Life and Robotics
Volume Number	22
Issue Number	2
e-ISSN	16147456
Language	English
Publisher	Springer Japan
Publisher Date	2017-01-03
Publisher Place	Tokyo
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Recommendation system Access log analysis Gradient Boosting Decision Tree Artificial Intelligence (incl. Robotics) Computation by Abstract Devices Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Biochemistry, Genetics and Molecular Biology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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