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.

中文翻译：

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MV-RNN：用于顺序推荐的多视图循环神经网络

顺序推荐是网络应用的一项基本任务，由于用户反馈不足，它通常会遇到项目冷启动问题。目前流行的方法有三种，分别是基于协同过滤的矩阵分解（MF）、马尔可夫链（MC）和循环神经网络（RNN）。虽然被广泛使用，但它们有一些局限性。基于 MF 的方法无法捕捉动态用户的兴趣。强马尔可夫假设极大地限制了基于 MC 的方法的性能。基于 RNN 的方法仍处于合并附加信息的早期阶段。基于这些基本模型，许多带有附加信息的方法只能验证以单独的方式合并一种模态。在这项工作中，为了进行顺序推荐并处理项目冷启动问题，我们提出了多视图循环神经网络（MV-RNN）模型。鉴于潜在特征，MV-RNN 可以通过结合视觉和文本信息来缓解项目冷启动问题。首先，在 MV-RNN 的输入端，研究了三种不同的多视图特征组合，如串联、加法融合和重构原始多模态数据融合。MV-RNN 应用循环结构来动态捕捉用户的兴趣。其次，我们在 MV-RNN 的隐藏状态上设计了一个单独的结构和一个统一的结构，以探索一种更有效的处理多视图特征的方法。在两个真实世界数据集上的实验表明，MV-RNN 可以有效地生成个性化排名列表，解决缺失模态问题，