Corpus ID: 38864450

Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network

@inproceedings{Seo2017RepresentationLO,
  title={Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network},
  author={Sungyong Seo and Jing Huang and H. Yang and Y. Liu},
  year={2017}
}
  • Sungyong Seo, Jing Huang, +1 author Y. Liu
  • Published 2017
  • It is common nowadays for e-commerce websites to encourage their users to rate shopping items and write review text. This review text information has been proven to be very useful in understanding user preferences and item properties, and thus enhances the capability of these websites to make personalized recommendations. In this paper, we propose to model user preferences and item properties using a convolutional neural network (CNN) with attention, motivated by the huge success of CNN for… CONTINUE READING

    Figures and Tables from this paper.

    Deep Learning Based Recommender System
    • 388
    • Highly Influenced
    • Open Access
    TransNets: Learning to Transform for Recommendation
    • 109
    • Open Access
    Deep Learning based Recommender System: A Survey and New Perspectives
    • 143
    • Highly Influenced
    • Open Access
    A review on deep learning for recommender systems: challenges and remedies
    • 56
    • Open Access
    TransRev: Modeling Reviews as Translations from Users to Items
    • 11
    • Highly Influenced
    • Open Access
    Hierarchical User and Item Representation with Three-Tier Attention for Recommendation
    • 7
    • Open Access
    Attentive Contextual Denoising Autoencoder for Recommendation
    • 12
    • Open Access
    Survey on Deep Learning Based Recommender Systems
    • 8
    • Highly Influenced
    • Open Access

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 19 REFERENCES
    Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
    • 3,792
    • Highly Influential
    • Open Access
    Enriching Word Vectors with Subword Information
    • 3,605
    • Open Access
    Hidden factors and hidden topics: understanding rating dimensions with review text
    • 1,037
    • Highly Influential
    • Open Access
    Hierarchical Attention Networks for Document Classification
    • 2,058
    • Highly Influential
    • Open Access
    Collaborative Deep Learning for Recommender Systems
    • 881
    • Open Access
    Character-level Convolutional Networks for Text Classification
    • 2,124
    • Highly Influential
    • Open Access
    Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
    • 866
    • Open Access
    Image-Based Recommendations on Styles and Substitutes
    • 849
    • Open Access
    A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval
    • 444
    • Open Access
    Inferring Networks of Substitutable and Complementary Products
    • 471
    • Open Access