Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/183327
Title: A restaurant recommender system for a new-born app-based gastronomic guide
Author: Granatiero, Pablo
Director/Tutor: Hernández-González, Jerónimo
Martín Suárez, David
Keywords: Sistemes d'ajuda a la decisió
Algorismes computacionals
Restaurants
Treballs de fi de màster
Aplicacions mòbils
Decision support systems
Computer algorithms
Restaurants
Master's theses
Mobile apps
Issue Date: 1-Jul-2021
Abstract: [en] This is a project of applied data analysis made in collaboration with the CPO of the restaurants app Velada. We analyze the data collected by the app with the objective of building a recommender system for its restaurants. The initial objective was to give particular attention to the parameter time, building a model able to make the right recommendation in the right moment for each user of the app. Different attempts have been performed, using both collaborative filtering and content based recommender systems, with different types of information as data input. We show that the best approach is a binary collaborative filter, although the results are preliminary due to the lack of enough data. We also show that the performance will be easily improved as new data becomes available. Finally, we provide some insights on how the problem of making a recommendation for a restaurant just in time could be solved in the future.
Note: Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Any: 2021. Tutor: Jerónimo Hernández González i David Martín Suárez
URI: http://hdl.handle.net/2445/183327
Appears in Collections:Programari - Treballs de l'alumnat
Màster Oficial - Fonaments de la Ciència de Dades

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