Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

By Elena Smirnova, Flavian Vasile. 2017

173 citations recorded by the source corpus.

Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Model Architectures, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Yi Tay
6. Fajie Yuan
7. Kun Zhang
8. Naomie Binti Salim

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Naomie Binti Salim
6. Aminu Da’u
7. Kun Zhang
8. Fajie Yuan

In Management Science and Operations Research

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Fajie Yuan
7. Aminu Da’u
8. Naomie Binti Salim

In Model Architectures

1. Xiangnan He
2. Quan Z. Sheng
3. Shoujin Wang
4. Longbing Cao
5. Yan Wang
6. Matthew D. Hoffman
7. Rahul G. Krishnan
8. Dawen Liang

In Recommender Systems

1. Xiangnan He
2. Aminu Da’u
3. Naomie Binti Salim
4. Xiang Wang
5. Yi Tay
6. Longbing Cao
7. Yan Wang
8. Quan Z. Sheng

Citations in the corpus

• Variational Autoencoders for Collaborative Filtering (2018, methodology): “For example, it has been used in recurrent neural networks for session-based sequential recommendation [8, 15, 16, 42, 45] and in feedward neural networks applied to Youtube recommendation [9].”
• Deep Learning Based Recommender System (2017, background): “[133] proposed a context-aware session-based recommender system based on conditional RNNs.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “…[210], MANN [211], STAMP [212] Sequential Models RNN based models that rely on sessions to construct input and output p-RNN [202],KERL [193],CRNNs [213], NARM [214], [215], [216], [217], [201], Translation based models for modeling the correlations of consecutive items TransRec [46],PeterRec…”
• A Survey on Session-based Recommender Systems (2019, methodology): “Particularly, an RNN-based SBRS first models each ordered session context as a sequence of interactions within the context.”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, background): “Others include external information by introducing additional regularization (Zhuang et al. 2017; Liang et al. 2018; Suglia et al. 2017), user behavior (Chen et al. 2018), contextual information (Smirnova 2017) and auxiliary information (He et al. 2017) such as knowledge base and temporal signals.”
• A Simple Convolutional Generative Network for Next Item Recommendation (2018, methodology): “…comparison with content-or context-based sequential recommendation models, such as the 3D CNN recommender [30] and other RNN variants [9, 20, 25, 27]; (2) the GRURec baseline could be regarded as the state-of-the-art Improved GRURec [28] when dealing with the long-range session data sets…”