Yelp_Recommender_System_No1_Solution

• Introduction: This repo contains all files needed for building a recommender system based on 2019 Yelp Challenge Datasets. This is the No.1 solution in USC Viterbi Data Mining Competition.

• Goal: With existing business and user information, we try to build a model to predict the ratings of new business-user pairs.

Workflow of Model:

• Step1: With given business-business relationships, business-user relationships, and business-categories relationships, we build a graph that makes connection among them. Then, graph embedding technique is used to construct 200 dimension numeric vectors that can represent the relationships between them.

(File name: 'GraphEmbedding.py'; Output: 'VectorizedFeatures.csv')

• Step2: Categories of restaurants shouldn't be viewed as separated features. Therefore, I add them back to the business vectors as side information to enhance the expression of individual business.

(NOTE: Because the memory limit of Vocareum, I incorporate this step into training phase files.)

• Step3: Two machine learning models are trained based on the embedding factors, business features, and user features.

Model 1: XGBOOST (File name: 'competition_XGBOOST.py'; Output: 'XGBoostRegWithEmbedV4Long' - pickle file)

Model 2: CatBOOST (File name: 'competition_CatBOOST.py'; Output: 'CatGBMRegWithEmbedV4Long' - pickle file)

• Step4: Outputs of the models are combined. We replace prediction scores that >5 as 5 and prediction scores that <1 as 1 before combination and after combination. Then we get our final results!

Future Improvements:

• Enhanced Graph Embedding with Side Information always performs better than original graph embedding method.
• Higher dimension for embedding vectors always gives us more accuracy predictions.
• Light GBM performs better than Cat Boost.
• Other related information can be included. (e.g., NLP analysis for review text)
• More data will capture higher fluctruations.
• Tagging Techniques should be possible ways to further develop recommender system.

Special Notes:

• Please do not copy the codes directly with any purpose.
• In order to protect the copyright, we do not provide original datasets.
• If you want to get the pickle files that are used to generate final models, please contact the author.