Corpus ID: 13658602

Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation

@article{Song2018AugmentingRN,
  title={Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation},
  author={Younghun Song and Jae-Gil Lee},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.02983}
}
The recent adoption of recurrent neural networks (RNNs) for session modeling has yielded substantial performance gains compared to previous approaches. In terms of context-aware session modeling, however, the existing RNN-based models are limited in that they are not designed to explicitly model rich static user-side contexts (e.g., age, gender, location). Therefore, in this paper, we explore the utility of explicit user-side context modeling for RNN session models. Specifically, we propose an… Expand
A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems
TLDR
A hybrid algorithm is designed that retrofits and repurposes a pre-filtering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. Expand
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
TLDR
This survey illustrates the concept of sequential recommendation, proposes a categorization of existing algorithms in terms of three types of behavioral sequence, and summarizes the key factors affecting the performance of DL-based models and conducts corresponding evaluations to demonstrate the effects of these factors. Expand
Deep Learning for Sequential Recommendation
TLDR
The concept of sequential recommendation is illustrated, a categorization of existing algorithms in terms of three types of behavioral sequences are proposed, and the key factors affecting the performance of DL-based models are summarized. Expand
Deep Learning-Based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations
TLDR
A novel classification framework for sequential recommendation tasks is proposed, with which representative DL-based algorithms for different sequential recommendation scenarios are introduced. Expand

References

SHOWING 1-10 OF 17 REFERENCES
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
TLDR
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 parametrizing the hidden unit transitions as a function of context information is proposed. Expand
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
TLDR
It is shown that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline. Expand
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Expand
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
TLDR
A seamless way to personalize RNN models with cross-session information transfer is proposed and a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions is devised. Expand
Inter-Session Modeling for Session-Based Recommendation
TLDR
This work proposes a novel approach that extends an RNN recommender to be able to process the user's recent sessions, in order to improve recommendations, and is therefore able to deal with the cold start problem within sessions. Expand
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation
TLDR
This work shows based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets and ensures the scalability of the kNN method. Expand
Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture
TLDR
An encoder-decoder neural architecture to model user session and intent using browsing and purchasing data from a large e-commerce company and incorporates an attention mechanism to explicitly learn the more expressive portions of the sequences in order to improve performance. Expand
Product-Based Neural Networks for User Response Prediction
  • Yanru Qu, Han Cai, +4 authors J. Wang
  • Computer Science, Mathematics
  • 2016 IEEE 16th International Conference on Data Mining (ICDM)
  • 2016
TLDR
A Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between interfield categories, and further fully connected layers to explore high-order feature interactions. Expand
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt. Expand
Neural Factorization Machines for Sparse Predictive Analytics
TLDR
NFM seamlessly combines the linearity of FM in modelling second- order feature interactions and the non-linearity of neural network in modelling higher-order feature interactions, and is more expressive than FM since FM can be seen as a special case of NFM without hidden layers. Expand
...
1
2
...