An Online Activity Recommendation Approach based on the Dynamic Adjustment of Recommendation Lists

被引：5
|
作者：
Liu, Duen-Ren [1 ]
Chen, Kuan-Yu [1 ]
Chou, Yun-Cheng [1 ]
Lee, Jia-Huei [1 ]
机构：
[1] Natl Chiao Tung Univ, Inst Informat Management, Hsinchu 300, Taiwan
来源：
2017 6TH IIAI INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS (IIAI-AAI) | 2017年
关键词：
Recommender system; Online Recommendation; Data Mining; Latent Topic Model; Matrix Factorization; Dynamic Adjustment of Recommendation List; NONNEGATIVE MATRIX-FACTORIZATION;
D O I：
10.1109/IIAI-AAI.2017.60
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 non-negative 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.
引用
收藏
页码：407 / 412
页数：6
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