• Vikas Kewlani
• Diana Antony
• Riddhi Dedhia
• Harshil Joshi

Recommender systems are widely deployed to predict the preferences of users to items. They are widely used for helping users find books, movies and products in general. In this project, we are trying to design a restaurant recommendation system based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is based on a simple observation that a user’s preference for an item is affected by different aspects discussed in reviews. The system is based on Web Scrapping. Web scraping[1], web harvesting, or web data extraction are all synonyms and are based on the same concept of data scraping used for extracting data from websites. The scrapped data will be used for training the model, which will help the system to predict more efficiently and accurately.

: Recommendation system, Web scrapping, Natural language processing, Collaborative filtering.

"A Survey Paper on Generic Restaurant Recommendation Systemthrough NLP Using Web Scrapping ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.78-81, April-2019, Available :http://www.jetir.org/papers/JETIRAX06016.pdf

"A Survey Paper on Generic Restaurant Recommendation Systemthrough NLP Using Web Scrapping ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp78-81, April-2019, Available at : http://www.jetir.org/papers/JETIRAX06016.pdf