Given Users Recommendations Based on Reviews on Yelp
Shuwei Zhang, Maiqi Tang, Qingyang Zhang, Yucan Luo, Yuhui Zou

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.
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Code & Models
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Taxonomy
TopicsRecommender Systems and Techniques · Topic Modeling · Sentiment Analysis and Opinion Mining
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Linear Layer · Weight Decay · Adam · Softmax · Residual Connection · Dropout · Linear Warmup With Linear Decay · Layer Normalization
