Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3217-3217.
Abstract
Collaborative filtering is a general technique for recommender systems, aiming to provide users with personalized recommendations. However, it suffers from two severe issues known as data sparsity and cold start. In this research, we present two different solutions to ameliorate these issues. First, we propose a trust-aware recommender system to incorporate the ratings of trusted neighbors and to form a more complete rating profile for the active users. Second, we also propose a novel Bayesian similarity measure by taking into account both the direction and length of rating profiles. Both research work shows promising empirical results based on real-world data sets. Finally, we outline the future research on trust-based clustering methods to further alleviate the concerned problems.
@inproceedings{ijcai2013p495,
title = {Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems},
author = {Guo, Guibing},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3217--3217},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/495},
}
TY - CPAPER TI - Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems AU - Guo, Guibing T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3217 EP - 3217 UR - https://ijcai.org/proceedings/2013/495 ER -Copied
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