An Item based Geo-Recommender System Inspired by Artificial Immune Algorithms
Antonio Cabanas-Abascal (University Carlos III Madrid, Spain)
Eduardo García-Machicado (University Carlos III Madrid, Spain)
Lisardo Prieto-González (University Carlos III Madrid, Spain)
Antonio de Amescua Seco (University Carlos III Madrid, Spain)
Abstract: Nowadays, one of the most relevant features provided by in almost every web site is a recommender system. However, they are usually focused on the common characteristics of several items which are shared among the users without taking into account that there are other very important features, such as geo-position. To face this lack of such relevant factors, authors propose the usage of a useful system that will aid in tasks related to pattern detection and fast adaptability to changes: Artificial Immune System. A combination of both systems and the addition of a geographic component will provide a new solution to this problem, which will solve as well these issues as other ones like comparison tasks in big data.
Keywords: artificial immune systems, big data, geo-localization, item, recommender systems
Categories: H.1.1, H.1.2, H.3.1, H.3.2, H.3.3, H.3.5