Paper Info
Title
The 50/50 Recommender: A Method Incorporating Personality into Movie Recommender Systems.
Abstract
Recommendation systems offer valuable assistance with selecting products and services. This work checks the hypothesis that taking personality into account can improve recommendation quality. Our main goal is to examine the role of personality in Movie Recommender systems. We introduce the concept of combining collaborative techniques with a personality test to provide more personalized movie recommendations. Previous research attempted to incorporate personality in Recommender systems, but no actual implementation appears to have been achieved. We propose a method and developed the 50/50 recommender system, which combines the Big Five personality test with an existing movie recommender, and used it on a renowned movie dataset. Evaluation results showed that users preferred the 50/50 system 3.6% more than the state of the art method. Our findings show that personalization provides better recommendations, even though some extra user input is required upfront.
Year
DOI
Venue
2017
10.1007/978-3-319-65172-9_42
Communications in Computer and Information Science
Keywords
Field
DocType
Personalization,Collaborative filtering,Recommendation systems,Data mining
Recommender system,Big Five personality traits,Collaborative filtering,Information retrieval,Computer science,Personality test,Personalization,Personality
Conference
Volume
ISSN
Citations 
744
1865-0929
2
PageRank 
References 
Authors
0.35
6
2
Name
Order
Citations
PageRank
Orestis Nalmpantis120.35
Christos Tjortjis217324.40