Transforming Geo-Referenced Data in Contextual Information for Context-Aware Recommender Systems

A recommender system can be defined as an information filtering technology which can be used to output a ranking of items (e.g. products, places, etc) that are likely to be of interest to a user. Context-aware recommender systems makes recommendations by incorporating contextual information into the...

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Bibliographic Details
Published in2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) pp. 528 - 533
Main Authors Santana, Igor Andre, Suniga, Abner, Donini, Juliano, Vaccari Sundermann, Camila, Oliveira Rezende, Solange, Aurelio Domingues, Marcos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2018
Subjects
Clustering
Clustering algorithms
Context aware recommender systems
Context modeling
Contextual information
Georeferenced data
Libraries
Motion pictures
Proposals
Recommender systems
Transforms
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Summary:A recommender system can be defined as an information filtering technology which can be used to output a ranking of items (e.g. products, places, etc) that are likely to be of interest to a user. Context-aware recommender systems makes recommendations by incorporating contextual information into the recommendation process. However, there is a lack of automatic methods to obtain contextual information for such systems. In this work, we have proposed to apply clustering techniques to transform geo-referenced data (i.e. latitude and longitude) in contextual information (i.e. regions) to feed the contextual systems. We have evaluated our proposal in the Yelp dataset, which showed evidences that our contextual information can provide better recommendations.
DOI:10.1109/WI.2018.00-42