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Recommendation System for Restaurants

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Anusha Jayasimhan, Parikshith Rai, Yash Parekh, Ojas Patwardhan
10.5120/ijca2017914312

Anusha Jayasimhan, Parikshith Rai, Yash Parekh and Ojas Patwardhan. Recommendation System for Restaurants. International Journal of Computer Applications 167(6):23-25, June 2017. BibTeX

@article{10.5120/ijca2017914312,
	author = {Anusha Jayasimhan and Parikshith Rai and Yash Parekh and Ojas Patwardhan},
	title = {Recommendation System for Restaurants},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {167},
	number = {6},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {23-25},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume167/number6/27776-2017914312},
	doi = {10.5120/ijca2017914312},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

It is often perplexing for a person to decide which restaurant he must visit from a huge range of available options. There have been numerous suggestion frameworks accessible for issues like shopping, online video excitement, recreations, and so forth. Eateries and Dining is one territory where there is a major chance to prescribe feasting choices to clients in light of their inclinations and in addition recorded information. By developing a recommendation system which could help a user to decide which restaurant one should visit, the person can save a lot of his time, efforts and money and thus have a great experience and satisfaction. There are various factors based on which a user makes a decision of visiting a restaurant like the type of cuisine of the restaurant, the location of the restaurant, the ambiance, price range, popularity, ratings, etc. Such information is collected and made available on sites such as Yelp and Zomato. Using well rounded, open source dataset provided by Yelp which provides data not only of the restaurant reviews, but also user-level information on their preferred restaurants the aim is to build an efficient recommendation system for the Yelp users in the form of a software application and thus help them predict whether they will like visiting a restaurant or not by applying machine learning techniques and algorithms.

References

  1. Yelp, “www.yelp.com/academicdataset,” Yelp academic dataset, 2016.
  2. A. Gandhe, “Restaurant recommendation system,” cs229.stanford.edu, 2015.
  3. “A preference-based restaurant recommendation system for individuals and groups,” www.cs.cornell.edu, 2013.

Keywords

Recommendation system, SVM, Yelp dataset, feature selection