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Recurrent Recommender Networks
2017
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining - WSDM '17
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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]
2016
arXiv
pre-print
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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
2017
Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17
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2017 pre-print arXiv:1706.04148v4 fatcat:f53p35v4tbefbm3g5ajl3bnxbmOther Versions
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]
2016
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2020 pre-print arXiv:2010.00482v1 fatcat:6k5vke77trh7zdrtdsbo4dcyou
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]
2020
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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
2018
Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18
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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]
2019
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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]
2019
arXiv
pre-print
Preserved Fulltext
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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
2021
Applied Sciences
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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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