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In our project, we focus on NLP-based hybrid recommendation systems. Our datais from Yelp Data. For our hybrid recommendation system, we have two majorcomponents: the first part is to embed the reviews with the Bert model andword2vec model; the second part is the implementation of an item-basedcollaborative filtering algorithm to compute the similarity of each reviewunder different categories of restaurants. In the end, with the help ofsimilarity scores, we are able to recommend users the most matched restaurantbased on their recorded reviews. The coding work is split into several parts:selecting samples and data cleaning, processing, embedding, computingsimilarity, and computing prediction and error. Due to the size of the data,each part will generate one or more JSON files as the milestone to reduce thepressure on memory and the communication between each part.

Given Users Recommendations Based on Reviews on Yelp