Given Users Recommendations Based on Reviews on Yelp
This is an incremental improvement for users seeking personalized restaurant recommendations based on review data.
The paper tackles the problem of recommending restaurants to users by developing a hybrid NLP-based recommendation system using Yelp reviews, combining BERT and word2vec embeddings with item-based collaborative filtering to compute similarity scores and generate recommendations.
In our project, we focus on NLP-based hybrid recommendation systems. Our data is from Yelp Data. For our hybrid recommendation system, we have two major components: the first part is to embed the reviews with the Bert model and word2vec model; the second part is the implementation of an item-based collaborative filtering algorithm to compute the similarity of each review under different categories of restaurants. In the end, with the help of similarity scores, we are able to recommend users the most matched restaurant based on their recorded reviews. The coding work is split into several parts: selecting samples and data cleaning, processing, embedding, computing similarity, 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 the pressure on memory and the communication between each part.