MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation | |
Qiang Cui1![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Knowledge and Data Engineering (TKDE)
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2018-11 | |
期号 | no页码:no |
摘要 | Sequential recommendation is a fundamental task for network applications, and it usually suffers from the item cold start problem due to the insufficiency of user feedbacks. There are currently three kinds of popular approaches which are respectively based on matrix factorization (MF) of collaborative filtering, Markov chain (MC), and recurrent neural network (RNN). Although widely used, they have some limitations. MF based methods could not capture dynamic user’s interest. The strong Markov assumption greatly limits the performance of MC based methods. RNN based methods are still in the early stage of incorporating additional information. Based on these basic models, many methods with additional information only validate incorporating one modality in a separate way. In this work, to make the sequential recommendation and deal with the item cold start problem, we propose a Multi-View Rrecurrent Neural Network (MV-RNN) model. Given the latent feature, MV-RNN can alleviate the item cold start problem by incorporating visual and textual information. First, At the input of MV-RNN, three different combinations of multi-view features are studied, like concatenation, fusion by addition and fusion by reconstructing the original multi-modal data. MV-RNN applies the recurrent structure to dynamically capture the user’s interest. Second, we design a separate structure and a united structure on the hidden state of MV-RNN to explore a more effective way to handle multi-view features. Experiments on two real-world datasets show that MV-RNN can effectively generate the personalized ranking list, tackle the missing modalities problem and significantly alleviate the item cold start problem. |
关键词 | Multi-view Sequential Recommendation Recurrent Neural Network Cold Start |
收录类别 | SCI |
语种 | 英语 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23690 |
专题 | 模式识别实验室 |
通讯作者 | Shu Wu |
作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) and University of Chinese Academy of Sciences (UCAS) 2.University of Southern California |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Qiang Cui,Shu Wu,Qiang Liu,et al. MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation[J]. IEEE Transactions on Knowledge and Data Engineering (TKDE),2018(no):no. |
APA | Qiang Cui,Shu Wu,Qiang Liu,Wen Zhong,&Liang Wang.(2018).MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation.IEEE Transactions on Knowledge and Data Engineering (TKDE)(no),no. |
MLA | Qiang Cui,et al."MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation".IEEE Transactions on Knowledge and Data Engineering (TKDE) .no(2018):no. |
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崔强 正式接收后 TKDE2881260(979KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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