.png){kind=icon}
返回
Recurrent Translation-Based Network for Top-N Sparse Sequential Recommendation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2941083.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Fulfilling users' needs and increasing the retention rate of recommendation systems are challenging. Most users have consumed a few items in most systems. Translation-based model performs well on sparse datasets. However, a user and only single previous item are considered for the user suggestion of next items. Alternatively, recurrent neural network utilizes sequential dependency but performs poorly on sparse datasets. We unify both and propose Recurrent Translation-based Network (RTN). RTN utilizes sequences of users' consumed items without limiting interactions between items to the most recent one. The results of conducting experiments on real-world datasets show that RTN outperforms other state-of-the-art approaches on sparse datasets.
Keyword:
Recommender system
collaborative filtering
recurrent neural network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Can CD34 discriminate between benign and malignant hepatocytic lesions in fine-needle aspirates and thin core biopsies?.png)
CancerOAAI
CancerIF0
OAAI分享收藏Seismic anisotropy indicates organized melt beneath the Mid-Atlantic Ridge aids seafloor spreadingGeology
OAAI
GeologyIF0
OAAI分享收藏HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie RecommendationHI2Rec: 探索异构信息中的知识以进行电影推荐IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容