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Abstract

In this paper, we introduce a useful natural language model for improving recommendation systems using collaborative filtering algorithms on ordinal ratings data. Since their inception, recommendation systems have evolved from simple user-business-rating matrices to complex systems that can consume multiple dimensions. Using Yelp's competition data set, we explore extending these dimensions to include natural language by leveraging a dual neural network architecture to produce a new and improved star rating system which offers potential improvements to collaborative filtering based recommendation systems.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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