Given Users Recommendations Based on Reviews on Yelp

TL;DR

This paper presents a hybrid recommendation system for Yelp users that combines NLP techniques like BERT and Word2Vec with collaborative filtering to suggest restaurants based on user reviews.

Contribution

It introduces a novel combination of review embedding and collaborative filtering for personalized restaurant recommendations on Yelp.

Findings

Effective review embedding with BERT and Word2Vec

Successful implementation of item-based collaborative filtering

Improved recommendation accuracy based on review similarity

Abstract

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.