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Recurrent Recommender Networks

Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing
2017 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17  
We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories.  ...  Recommender systems traditionally assume that user profiles and movie attributes are static.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or other funding  ... 
doi:10.1145/3018661.3018689 dblp:conf/wsdm/WuABSJ17 fatcat:qggq72dolva35fvdudqtdepbui

Session-based Recommendations with Recurrent Neural Networks [article]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
2016 arXiv   pre-print
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.  ...  This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items.  ...  RECOMMENDATIONS WITH RNNS Recurrent Neural Networks have been devised to model variable-length sequence data.  ... 
arXiv:1511.06939v4 fatcat:6waofdr6ebbpvf6zisyi3dzpuy

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi
2017 Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17  
Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings.  ...  Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings.  ...  Session-based Recurrent Neural Network Our model is based on the session-based Recurrent Neural Network (RNN henceforth) model presented in [7] .  ... 
doi:10.1145/3109859.3109896 dblp:conf/recsys/QuadranaKHC17 fatcat:zlizm2zazjgilgojsfunlb7rwm

Improved Recurrent Neural Networks for Session-based Recommendations [article]

Yong Kiam Tan, Xinxing Xu, Yong Liu
2016 arXiv   pre-print
Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches.  ...  In this work, we further study RNN-based models for session-based recommendations.  ...  Recurrent Neural Networks (RNNs) were recently proposed in [10] for the session-based recommendation task.  ... 
arXiv:1606.08117v2 fatcat:s5qikrhp2ncyfgaymoh2kolrha

Interacting Attention-gated Recurrent Networks for Recommendation [article]

Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M.J. Tax
2017 arXiv   pre-print
To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step.  ...  Capturing the temporal dynamics of user preferences over items is important for recommendation.  ...  This paper proposes the Interacting Attention-gated Recurrent Network (IARN) to accommodate temporal context for better recommendation.  ... 
arXiv:1709.01532v2 fatcat:wawwv6jyrzecjhrd7uljb7slbq

Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks [article]

Arash Mahyari, Peter Pirolli
2021 arXiv   pre-print
The system, consisting of two inter-connected recurrent neural networks (RNNs), uses the history of workouts to recommend the next workout activity for each individual.  ...  In this paper, we propose an exercise recommendation system that also predicts individual success rates.  ...  We use these data to train a machine learning recommender technique that uses a combination of association rules and recurrent neural networks.  ... 
arXiv:2010.00482v2 fatcat:5n3ddw4mbbgingdck5xefgs4pi

Many-to-one Recurrent Neural Network for Session-based Recommendation [article]

Amine Dadoun
2020 arXiv   pre-print
We propose to use a many-to-one recurrent neural network that learns the probability that a user will click on an accommodation based on the sequence of actions he has performed during his browsing session  ...  This paper presents the D2KLab team's approach to the RecSys Challenge 2019 which focuses on the task of recommending accommodations based on user sessions.  ...  Recurrent Neural Network Recurrent neural networks are widely used for many NLP tasks such as named entity recognition, machine translation or semantic classification [12] .  ... 
arXiv:2008.11136v1 fatcat:iml6zo7qcrgcfkjyps4isvriay

Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks [article]

Elena Smirnova, Flavian Vasile
2017 arXiv   pre-print
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.  ...  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  ...  RELATED WORK 2.1 Recurrent Neural Networks for Recommendation Hidasi et al. [5] applied RNNs to the task of session-based recommendation.  ... 
arXiv:1706.07684v1 fatcat:owj72564ergn3emey27k2hnueq

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

Balázs Hidasi, Alexandros Karatzoglou
2018 Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18  
In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting.  ...  The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations.  ...  Recurrent Neural Networks have been used with success in the area of session-based recommendations; (Hidasi et al., 2016a) proposed a Recurrent Neural Network with a pairwise ranking loss for this task  ... 
doi:10.1145/3269206.3271761 dblp:conf/cikm/HidasiK18 fatcat:oaxbpaj2zvaetecp5inicz6t34

Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks [article]

Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha
2019 arXiv   pre-print
Keywords: News Recommender Systems, Session-based Recommendation, Artificial Neural Networks, Context-awareness, Hybridization  ...  Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input  ...  Recurrent Neural Networks (RNN) represent a natural choice for sequence prediction tasks, as they can learn models from sequential data.  ... 
arXiv:1904.10367v1 fatcat:sa66nqhy3nhxnkqvno4ttp3oie

Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation [article]

Lu Wang, Wei Zhang, Xiaofeng He, Hongyuan Zha
2018 arXiv   pre-print
In this paper, we propose Supervised Reinforcement Learning with Recurrent Neural Network (SRL-RNN), which fuses them into a synergistic learning framework.  ...  Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes.  ...  Recurrent Neural Network (SRL-RNN).  ... 
arXiv:1807.01473v2 fatcat:xfjqg6jjzjc6ve3n5yxesrajnq

Recurrent Latent Variable Networks for Session-Based Recommendation [article]

Sotirios Chatzis, Panayiotis Christodoulou, Andreas S. Andreou
2017 arXiv   pre-print
Our proposed approach consists in treating the network recurrent units as stochastic latent variables with a prior distribution imposed over them.  ...  In this work, we attempt to ameliorate the impact of data sparsity in the context of session-based recommendation.  ...  We evaluate the e cacy of the so-obtained approach, dubbed Recurrent Latent Variable Network for Session-Based Recommendation (ReLaVaR), considering a challenging publicly available benchmark.  ... 
arXiv:1706.04026v1 fatcat:oukh636zofe3vjc7jhzv5dqnui

Hierarchical Context Enabled Recurrent Neural Network for Recommendation

Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.  ...  The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts.  ...  Hierarchical Context Recurrent Neural Network We propose HCRNN, a modification of the RNN structure, to model three hierarchical contexts optimized for recommendations, which we describe in this section  ... 
doi:10.1609/aaai.v33i01.33014983 fatcat:b4i3gbjfinfi3eirdnxe4ubcoa

Hierarchical Context enabled Recurrent Neural Network for Recommendation [article]

Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon
2019 arXiv   pre-print
We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.  ...  The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts.  ...  Hierarchical Context Recurrent Neural Network We propose HCRNN, a modification of the RNN structure, to model three hierarchical contexts optimized for recommendations, which we describe in this section  ... 
arXiv:1904.12674v1 fatcat:35dpr6ixd5hl5naatmj5pgpxqa

Code Edit Recommendation Using a Recurrent Neural Network

Seonah Lee, Jaejun Lee, Sungwon Kang, Jongsun Ahn, Heetae Cho
2021 Applied Sciences  
In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN).  ...  CERNN forms contexts that maintain the sequence of developers' interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution  ...  Recurrent Neural Network A recurrent neural network (RNN) is a type of neural network that specializes in processing sequential data [28, 29] .  ... 
doi:10.3390/app11199286 fatcat:in7twfrkvrh4jb3tm6ndfcaali
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