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Modeling Uncertainty Driven Curiosity for Social Recommendation


Authors: Q. Wu, S. Liu, and C. Miao
Title: Modeling Uncertainty Driven Curiosity for Social Recommendation
Abstract: Most of the current recommender systems focus on estimating user preferences. However, a person's interest in an item is not determined by his/her preference alone. Psychological research has shown that curiosity is a critical motivation relating to a person's interests and driving explorative behaviours. Motivated as above, we aim to model user curiosity in social recommendation context. In this work, we model uncertainty driven curiosity, wherein uncertainty is a well acknowledged factor that stimulates human curiosity. We model user uncertainty based on two well-known theories of uncertainty, i.e., Shannon entropy and Damster-Shafter theory. Then, we rank items by consolidating both user preference and user uncertainty using weighted Borda count. The proposed model is evaluated with two large-scale real world datasets, Douban and Flixster. The experimental results highlight that uncertainty driven curiosity has a positive impact on personalized ranking, by remarkably improving recommendation precision and diversity.
Keywords: Uncertainty; Curiosity; Social recommendation; Precision; Diversity; Coverage
Conference Name: 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI'17)
Location: Leipzig, Germany
Publisher: ACM
Year: 2017
Accepted PDF File: Modeling_Uncertainty_Driven_Curiosity_for_Social_Recommendation_accepted.pdf
Permanent Link: https://dx.doi.org/10.1145/3106426.3106475
Reference: Q. Wu, S. Liu, and C. Miao, “Modeling uncertainty driven curiosity for social recommendation,” in Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI’17). ACM, August 2017, pp. 790–798.
bibtex: 
@inproceedings{LILY-c132, 
    author = {Wu, Qiong and Liu, Siyuan and Miao, Chunyan},
    title  = {Modeling Uncertainty Driven Curiosity for Social Recommendation},  
    booktitle = {Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI'17)}, 
    year  = {2017}, 
    month = {August}, 
    pages = {790-798}, 
    location = {Leipzig, Germany},
    publisher = {ACM},
 }