NDLI: A Novel Approach Towards Context Sensitive Recommendations Based on Machine Learning Methodology

Content Provider	IEEE Xplore Digital Library
Author	Kothari, A.A. Patel, W.D.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Parul Inst. of Technol., Vadodara, India (Kothari, A.A.; Patel, W.D.)
Abstract	Contexts and aspects have been distinguished as the significant factors in fabricating recommender systems. Most recommender systems aim at utilizing either non-contextual preferences or contextual preferences distinctly, while very few endeavors have been made to identify the significance of both. Hence an attempt has been made to study the influence of both, users' context dependent and context independent preferences in the single recommender system. In this case, accuracy has always been a challenge. Therefore, there exists a need for such a classification technique which can be commonly applied to both types of preferences that helps enhance the accuracy of the retrieved results. For this purpose, use of a standard Machine learning technique well known as Support Vector Machines was proposed. The idea behind using Support vector machines is to split the data in an optimal way and classify the data precisely to aid prediction purpose. For generating recommendations, these context-dependent preferences are further combined with users' context-independent preferences. Finally this technique is applied on a real-life dataset to demonstrate that our method is proficient in dealing with contextual preferences of users and well classify them to achieve better recommendation accuracy than the relative works.
Sponsorship	IETE Gwalior Sub Center
Starting Page	1114
Ending Page	1118
File Size	427005
Page Count	5
File Format	PDF
e-ISBN	9781479917976
DOI	10.1109/CSNT.2015.191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Context Training Accuracy Support Vector Machine Opinion mining Recommender System Data mining Recommender systems
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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