[u' @inproceedings{almeida_aspect-based_2012, address = {Barcelona, Spain}, title = {An {Aspect}-{Based} {Resource} {Recommendation} {System} for {Smart} {Hotels}}, isbn = {978-1-61208-236-3}, abstract = {The number of resources (services, data, multimedia content, etc) available in Smart Spaces can ver overwhelming. Finding the desired resource can be a tedious and difficult task. In order to solve this problem, Smart Spaces contain much information that can be employed to filter these resources. Using the user context-data available in Smart Spaces can help refining and enhancing the recommendation process, providing more relevant results. To help users finding the most suitable resource we have developed a recommendation system that takes into account both user and resource features and context data like the location or current activity. This recommendation system is flexible enough to be applied to different types of resources and domains. In this paper we describe the resource aspects identified to be used in the recommendation system and how they are combined to create a metric that allows us to select the best resource for each situation.}, booktitle = {Proceedings of {The} {Sixth} {International} {Conference} on {Mobile} {Ubiquitous} {Computing}, {Systems}, {Services} and {Technologies}}, author = {Almeida, Aitor and Castillejo, Eduardo and L\xf3pez-De-Ipi\xf1a, Diego and Sacrist\xe1n, Marcos and Diego, Javier}, month = sep, year = {2012}, keywords = {Artificial Intelligence, Context-Aware Computing, Data analysis, accessibility, core-c, intelligent environments, machine learning, markov chains, nearest neighbor, recommendation systems, thofu, weka} }']