Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/118920
Title: Collaborative filtering employing users’ interactions in web applications
Author: Galindo Martínez, Sara
Director/Tutor: Puertas i Prats, Eloi
Keywords: Sistemes d'ajuda a la decisió
Sistemes experts (Informàtica)
Programari
Treballs de fi de grau
Aprenentatge automàtic
Intel·ligència econòmica
Decision support systems
Expert systems (Computer science)
Computer software
Bachelor's thesis
Machine learning
Business intelligence
Issue Date: 26-Jan-2017
Abstract: Currently, thanks to the Internet anyone has access to a large amount of data and for this reason it is essential to create new systems that help to understand that information in a little time. Recommender Systems are engines which allow to filter the information depending on people’s interests. There are different kinds of Recommenders and each of them has a different purpose. In this project, a case of use of a Collaborative filtering Recommender System is introduced employing every interaction users do while surfing the Stilavia web site as data input. In order to carry out this task, some scoring functions are required to generate a model. This model will be extrapolated throughout the whole dataset space thanks to a Machine Learning algorithm called Alternating Least Squares (ALS) that is available in a library of the Apache Spark framework. Lastly, the results of each scoring function will be tested and evaluated employing a statistic estimator.
Note: Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2017, Director: Eloi Puertas i Prats
URI: http://hdl.handle.net/2445/118920
Appears in Collections:Treballs Finals de Grau (TFG) - Enginyeria Informàtica

Files in This Item:
File Description SizeFormat 
memoria.pdfMemòria2.02 MBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons