Restaurant Recommendations Based on a Domain Model and Fuzzy Rules

This research proposes a hybrid recommender system for restaurants that uses fuzzy inference systems together with collaborative filtering and content-based techniques, considering the expert’s experience, the ratings given by similar users and restaurant model. Content-based technique seeks to alleviate the cold-start problem, which commonly arises in collaborative filtering. The goal is to help each user to find interesting restaurants in the city. To evaluate the recommender system a data set of 50 users and 60 restaurants was tested. Was used RMSE for obtain the accuracy in the recommendations.

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