Paper Info
Title
A Personality-Based Recommender System For Semantic Searches In Vehicles Sales Portals
Abstract
This work proposes a personality-based recommender system to implement semantic searches on Internet Vehicles Sales Portals. The system is based on a typical recommender system architecture that has been extended to combine a hybrid recommendation approach with a machine learning classifier technique (k-NN). It proposes a combination of the Five Factor Model (Big Five Model) with a correlation between car fronts and power and sociability perceptions. A prototype was implemented to answer the semantic searches considering personality-based user's profiles and a set of Brazilian cars. After each search, a questionnaire was provided for the users to verify how successful the recommendations were for them. The prototype received web searches during a period of 15 days. The final report showed that 77.67% of the users accepted the personality-based recommendations, what indicates that the proposed approach could be promising to improve the quality of the recommendations on the user's point of view.
Year
DOI
Venue
2017
10.1007/978-3-319-59650-1_51
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2017
Keywords
Field
DocType
Recommender system, Personality traits, Semantic searches
Recommender system,Big Five personality traits,World Wide Web,Architecture,Computer science,Perception,Learning classifier system,Personality,The Internet
Conference
Volume
ISSN
Citations 
10334
0302-9743
1
PageRank 
References 
Authors
0.36
14
3
Name
Order
Citations
PageRank
Fábio A. Procópio de Paiva122.06
José Alfredo F. Costa25210.11
msc claudio r m silva322.40