@inproceedings{8c02491434c04c28b3e6facb1eae820c,
title = "An Online Activity Recommendation Approach Based on the Dynamic Adjustment of Recommendation Lists",
abstract = "This research investigates an online recommendation method for new types of online news websites. Cross-domain analysis on user browsing news and the attending activities is conducted to predict user preferences on activities based on nonnegative Matrix Factorization (NMF) and Latent Dirichlet Allocation (LDA) topic model. A novel approach is proposed for the dynamic adjustment of recommendation lists in order to tackle the issue of limited recommendation layouts. The existing studies have not addressed this issue. The proposed approach is implemented on an online news website and evaluated for online recommendations. The experiment results demonstrate that our method can predict user preferences on recommended activities and enhance the effectiveness of recommendations.",
keywords = "Data Mining, Dynamic Adjustment of Recommendation List, Latent Topic Model, Matrix Factorization, Online Recommendation, Recommender system",
author = "Duen-Ren Liu and Chen, {Kuan Yu} and Chou, {Yun Cheng} and Lee, {Jia Huei}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017 ; Conference date: 09-07-2017",
year = "2017",
month = nov,
day = "15",
doi = "10.1109/IIAI-AAI.2017.60",
language = "English",
series = "Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "407--412",
editor = "Kiyota Hashimoto and Naoki Fukuta and Tokuro Matsuo and Sachio Hirokawa and Masao Mori and Masao Mori",
booktitle = "Proceedings - 2017 6th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2017",
address = "United States",
}