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Analyzing Temporal, Spatial, and Historical Data in Rating Prediction Algorithms: A Comparative Study

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Analyzing Temporal, Spatial, and Historical Data in Rating Prediction Algorithms: A Comparative Study

Fall 2022, CSE 258: Recommender Systems and Web Mining, UC San Diego

In this project, we utilize the Google Local Reviews data set to predict the potential rating a user will give to a business. Specifically, we compare and contrast the effects of including temporal and spatial aspects of the data in our models for the task at hand.

We show that the performance of machine/deep learning models improve when we provide temporal, spatial as well as historical data. In other words, additional metadata improves the predictive power of a regression model.

Please refer to Report for more details.

Contributors

  1. Sanidhya Singal
  2. Manas Sharma
  3. Shyam Renjith

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  • Jupyter Notebook 99.7%
  • Python 0.3%