Experimental Analysis of Recommendation System in e-Commerce
Neha Verma1, Devanand2, Bhavna Arora3
1Neha Verma, M. Tech Student, Department of Computer Science & IT, Central University of Jammu, Jammu, India.
2Devanand, Ph.D., Department of Computer Science & IT, Central University of Jammu, Jammu, India.
3Bhavna Arora, Ph.D., Department of Computer Science & IT, Central University of Jammu, Jammu, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 121-127 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10330688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In our research paper with the use of a well-executed augmented reality (AR) marker in which we are using the combined properties of QR code and AR to scan the QR code associated with the particular restaurant menu. Scanning will result in obtaining of 3-D images of all the dishes present in that restaurant along with the dish details like health constituents, servings, how a particular dish tastes like and many more details associated with that dish. In this way we are elevating the dine-in experience by removing the traditional menu ordering process. It is directed at eliminating the discomfort customers are facing due to food and language gaps. Visual representation of food will give unique experience and due to this wide range of customers will prefer to come to restaurants. So overall it will revolutionize the conventional ordering system which is followed in the restaurants.
Keywords: Data Mining, Recommendation System, Recommendation Technique, e-commerce.
Scope of the Article: Data Mining