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A Social Curiosity Inspired Recommendation Model to Improve Precision, Coverage and Diversity


Authors: Q. Wu, S. Liu, C. Miao, Y. Liu, and C. Leung
Title: A Social Curiosity Inspired Recommendation Model to Improve Precision, Coverage and Diversity
Abstract: With the prevalence of social networks, social recommendation is rapidly gaining popularity. Currently, social information has mainly been utilized for enhancing rating prediction accuracy, which may not be enough to satisfy user needs. Items with high prediction accuracy tend to be the ones that users are familiar with and may not interest them to explore. In this paper, we take a psychologically inspired view to recommend items that will interest users based on the theory of social curiosity and study its impact on important dimensions of recommender systems. We propose a social curiosity inspired recommendation model which combines both user preferences and user curiosity. The proposed recommendation model is evaluated using large-scale real world datasets and the experimental results demonstrate that the inclusion of social curiosity significantly improves recommendation precision, coverage and diversity.
Keywords: 
Conference Name: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI'16)
Location: Omaha, USA
Publisher: IEEE
Year: 2016
Accepted PDF File: A_Social_Curiosity_Inspired_Recommendation_Model_to_Improve_Precision_Coverage_and_Diversity_accepted.pdf
Permanent Link: https://doi.org/10.1109/WI.2016.0042
Reference: Q. Wu, S. Liu, C. Miao, Y. Liu, and C. Leung, “A social curiosity inspired recommendation model to improve precision, coverage and diversity,” in Proceedings of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI’16). IEEE, October 2016, pp. 240–247.
bibtex: 
@inproceedings{LILY-c105, 
    author	= {Wu, Qiong and Liu, Siyuan and Miao, Chunyan and Liu, Yuan and Leung, Cyril},
    title	= {A Social Curiosity Inspired Recommendation Model to Improve Precision, Coverage and Diversity},  
    booktitle	= {Proceedings of the 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI'16)}, 
    year		= {2016}, 
    month	= {October}, 
    pages	= {240-247}, 
    location	= {Omaha, USA},
    publisher	= {IEEE},
 }