Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Naïve filterbots for robust cold-start recommendations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors
Interacting with Computers
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Improving new user recommendations with rule-based induction on cold user data
Proceedings of the 2007 ACM conference on Recommender systems
A comparative user study on rating vs. personality quiz based preference elicitation methods
Proceedings of the 14th international conference on Intelligent user interfaces
Embedding Emotional Context in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Evaluating Interface Variants on Personality Acquisition for Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Blog tells what kind of personality you have: egogram estimation from Japanese weblog
Proceedings of the ACM 2011 conference on Computer supported cooperative work
A study on user perception of personality-based recommender systems
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
An automatic classification system for consumer regulatory focus by analyzing web shopping logs
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Using personality to adjust diversity in recommender systems
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Method for extraction of characteristics of personal characters from life log
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
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Collaborative filtering (CF), one of the most successful recommendation approaches, continues to attract interest in both academia and industry. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem, a situation where historical data is too sparse (known as the sparsity problem), new users have not rated enough items (known as the new user problem), or both. In this paper, we aim at addressing the cold-start problem by incorporating human personality into the collaborative filtering framework. We propose three approaches: the first is a recommendation method based on users' personality information alone; the second is based on a linear combination of both personality and rating information; and the third uses a cascade mechanism to leverage both resources. To evaluate their effectiveness, we have conducted an experimental study comparing the proposed approaches with the traditional rating-based CF in two cold-start scenarios: sparse data sets and new users. Our results show that the proposed CF variations, which consider personality characteristics, can significantly improve the performance of the traditional rating-based CF in terms of the evaluation metrics MAE and ROC sensitivity.