Volume : 4, Issue : 4, MAR 2020
RESTAURANT RECOMMENDATION SYSTEM USING MACHINE LEARNING
Mr. N. Varatharajan, J. Guruprasad, K. Mathumitha
Recommendation system is widely used for deploying the prediction based on the preferences of users to items. In many places, to find a restaurant in rush hour is a challenging activity to do. It is going to be easy, when someone accesses an application and it recommend the best restaurant then he/she can visit to. In this paper, according to the characteristics of restaurant recommender systems, the restaurant recommendation is done based on improved collaborative and content-based filtering method is proposed to be analyzing the end user’s behaviors. The ICCFM implicitly or explicitly considers the influences of individual or similar user preferences and the relationship. Then this experiment is executed on the yelp data set that our application crawls. This recommender system propose a machine learning algorithms to resolve the issues of personalized restaurant selection relying upon yelp data.
Recommender system; machine learning collaborative filtering; content-based filtering; user inputs and behaviors; user feature.
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- Anant Gupta; Kuldeep Singh 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI) “Location based personalized restaurant recommendation system for mobile environments”
- Ahsan Habib; Md. Abdur Rakib; Mohammad Abul Hasan 2016 19th International Conference on Computer and Information Technology (ICCIT). “Location, time, and preference aware restaurant recommendation method”
- Jun Zeng; Feng Li; Haiyang Liu; Junhao Wen; Sachio Hirokawa 2016 5th IIAI International conference on Advanced Applied Informatics (IIAI-AAI) “A Restaurant Recommender System Based on User Preference and Location in Mobile Environment”
- Rahul Katarya; Om Prakash Verma 2015 International Conference on Green Computing and Internet of Things (ICGCIoT) “Restaurant recommender system based on psychographic and demographic factors in mobile environment”
- Anu Taneja; Prashant Gupta; Aayush Garg; Akhil Bansal; Kawal Preet Grewal; Anuja Arora 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) “Social graph-based location recommendation using users' behavior: By locating the best route and dining in best restaurant”
- Lee, H. Kim, J. Jung, G. Jo (2006) 430 – 438.“Location-Based Service Context Data for a Restaurant Recommendation”
- Sarwar, G. Karypis, J. Konstan, and J. Riedl, in Proceedings of the 10th international conference on World Wide Web. ACM, 2001, pp. 285– “Item-based collaborative filtering recommendation algorithms,”
- Bao, Y. Zheng, and M. F. Mokbel, in Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012, pp. 199–208.“Locationbased and preference-aware recommendation using sparse geosocial networking data,”
- Zhichao Chang, Mohammad Shamsul Arefin, Yasuhiko Morimoto 2013 Second IIAI International Conference on Advanced Applied Informatics “Hotel Recommendation Based On Surrounding Environments”
- Yelp, “www.yelp.com/academicdataset,” Yelp academic dataset, 2016.
- Gandhe, “Restaurant recommendation system,” cs229.stanford.edu, 2015.
- “A preference-based restaurant recommendation system
- Roy, S. Banerjee, M. Sarkar, A. Darwish, and M. Elhoseny, et al. "Exploring new vista of intelligent collaborative filtering: a restaurant recommendation paradigm", Joumal of Computational Science, 2018 " in press" .
- Cui, P. Wang, X. Chen, D Yi, , and D. Guo, et al. "How to use the social media data in assisting restaurant recommendation", Intem ational Conference on Database Systems for Advanced Applications. Springer, Cham, 2016, pp. 134-141.