The BPSO Based Complex Splitting of Context-Aware Recommendation

被引：2

作者：

Yang, Shuxin ^{[1
]}

Peng, Qiuying ^{[1
]}

Chen, Le ^{[1
]}

机构：

[1] Jiangxi Univ Sci & Technol, Sch Informat Engn, Ganzhou 341000, Peoples R China

来源：

COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS, (ISICA 2015) | 2016年 / 575卷

关键词：

Context-aware recommendation; Complex splitting; Particle swarm optimization; Collaborative filtering;

D O I：

10.1007/978-981-10-0356-1_46

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Item Splitting splits an item into two items rated under two alternative contextual conditions respectively for improving the prediction accuracy of contextual recommendations. To get more specialized rating data, Complex Splitting is proposed to further improve the accuracy of recommendations. The key of the approach is to select multiple contextual conditions for splitting user or item. We translate it into a contextual conditions combinatorial optimization problem based on discrete binary particle swarm optimization (BPSO) algorithm. The item or user is split into two different items or users according to those contextual conditions in optimal combination. We evaluate our algorithm through a real world dataset and the experimental results demonstrate its validity and reliability.

引用

页码：435 / 444

页数：10

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