Un sistema de recomendación basado en perfiles generados por agrupamiento y asociaciones
[EN] "This article might interest you", "People who bought this article, also bought ..." or "This might also interest you" are phrases that are increasingly present in the daily activity of users that consume products and services in virtual sto...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | español |
| OAI Identifier: | oai:riunet.upv.es:10251/94049 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/94049 |
| Access Level: | acceso abierto |
| Palabra clave: | Association rules Clustering Data mining Rules induction Recommendations System Data Science Sistema de recomendaciones Inducción de reglas Minería de datos Agrupamiento Reglas de asociación Mineria de dades Sistemes de recomanació Agrupament Regles d&apos associació LENGUAJES Y SISTEMAS INFORMATICOS Máster Universitario en Ingeniería y Tecnología de Sistemas Software-Màster Universitari en Enginyeria i Tecnologia de Sistemes Programari |
| Sumario: | [EN] "This article might interest you", "People who bought this article, also bought ..." or "This might also interest you" are phrases that are increasingly present in the daily activity of users that consume products and services in virtual stores and other services. The reason for it is that recommendation systems have been consolidated as a strong trend for the growth of digital commerce in recent years, especially with regards to big data. Advances and the cheapening of technologies have allowed many companies to build their virtual environments to complement their physical store, or they were even created in a purely virtual plan and, as a result, in both cases, the interactions of customers with their virtual environments have stored a huge amount of data about their preferences, such as searched and / or purchased products, movies or songs played or marked as favorites, or even events captured by sensors and registered in a database, since the Internet Things is an agent present and in great ascension that records data about people and their choices involuntarily. The main objective of this work is to use the existing data about users’ preferences, and apply Machine Learning techniques to develop a system to make recommendations, suggesting new items adjusted to users’ tastes based on users’ profiles or products generated for this purpose. Currently, several service providers already offer SaaS solutions (Software as a Service) to companies in order to integrate personalized recommendations into their commercial project. Among the best known are BrainSINS7 , Barilliance8 or Certona9 . Unlike the service offered by these providers, our proposal is very non-invasive in the sense that it only uses the data of the clients' election history, without other data about them being necessary. To do this, we propose using a hybrid method that combines collaborative and item-to-item filtering. Besides security due to customers’ anonymity and for not demanding the challenging task of obtaining items ratings through customers, our approach aims to be able to make recommendations without requiring much training data, which automatically makes it a simple and low-cost solution, both for developing and maintaining the recommendation system, which can be potentially interesting for companies that remain excluded from the world of recommendations. |
|---|