Neural Survival Recommender

By How Jing, Alex Smola. 2017

166 citations recorded by the source corpus.

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

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. Julian McAuley
4. Meng Wang
5. Xing Xie
6. Tat-seng Chua
7. Le Wu
8. Yi Tay

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Julian McAuley
5. Le Wu
6. Xing Xie
7. Yongfeng Zhang
8. Tat-seng Chua

In Management Science and Operations Research

1. Xiang Wang
2. Wang-Cheng Kang
3. Julian McAuley
4. Meng Wang
5. Jiacheng Li
6. Xiangnan He
7. Ruining He
8. Le Wu

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “Survival analysis [18, 30] has been applied in the field of estimating user return time [20].”
• Reinforcement Learning based Recommender Systems: A Survey (2021, methodology): “Authors use survival models [186] to model user return [187] and user activeness.”
• Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018, methodology): “This is essentially equivalent to time representation described in [25] and [49].”
• Top-K Off-Policy Correction for a REINFORCE Recommender System (2018, background): “…line of work that is closely re-lated to ours is the growing body of literature on applying deep neural networks to recommender systems [11, 16, 37], in particular using recurrent neural networks to incorporate temporal information and historical events for recommendation [6, 17, 20, 45, 49].”
• A Survey on Session-based Recommender Systems (2019, methodology): “, purchase or click, and thus each interaction in the output is further simplified to an item [43, 49].”
• Machine Learning for Survival Analysis (2019, background): “In addition, Recurrent Neural Network (RNN)-based approaches have been successfully combined with survival analysis for studying recurring events, particularly in the context of user behavior modeling applications (Jing and Smola 2017; Yang et al. 2018).”
• Time Interval Aware Self-Attention for Sequential Recommendation (2020, methodology): “RNNs (Recurrent Neural Network) based models like [3, 12, 13, 18] use RNNs to model entire user sequences.”
• Deep Learning Based Recommender System (2017, background): “Among the reviewed studies, several works [5, 73, 87, 188] also applied multitask learning to recommender systems in a deep neural framework and achieved some improvements over single-task learning.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “Recurrent Recommender Networks (RRN) is one of the Recurrent neural networks to capture temporal evolution ARSE [153],RRN [204] [205], [206], [207], [208], [209] Memory network based models NMRN [210], MANN [211], STAMP [212] Sequential Models RNN based models that rely on sessions to construct…”
• Artificial intelligence in recommender systems (2020, background): “LSTM is also used in recommender systems to make in-time music recommendations, to predict when users will return to a music system and what their interest will be at that time [95].”
• Translation-based Recommendation (2017, background): “Recently, Recurrent Neural Networks (RNN) are introduced into recommender systems to capture temporal dynamics [Hidasi et al. , 2016; Wu et al. , 2017; Jing and Smola, 2017].”
• DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades (2017, methodology): “Deep learning technique was also employed to learn the function of arrival rate in point process [30, 31].”
• Self-Attentive Sequential Recommendation (2018, methodology): “Other than MC-based methods, another line of work adopts RNNs to model user sequences [2], [23]–[25].”