Hybrid neural recommendation with joint deep representation learning of ratings and reviews
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Fangzhao Wu | Qiyao Peng | Pengfei Jiao | Lin Pan | Hongtao Liu | Lin Gan | Yian Wang | Fangzhao Wu | Lin Pan | Pengfei Jiao | Qiyao Peng | Hongtao Liu | Yian Wang | Lin Gan
[1] Yehuda Koren,et al. Factorization meets the neighborhood: a multifaceted collaborative filtering model , 2008, KDD.
[2] Yiqun Liu,et al. Rating-Boosted Latent Topics: Understanding Users and Items with Ratings and Reviews , 2016, IJCAI.
[3] MengChu Zhou,et al. Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors , 2020, IEEE Transactions on Cybernetics.
[4] Chong Wang,et al. Collaborative topic modeling for recommending scientific articles , 2011, KDD.
[5] Christopher D. Manning,et al. Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.
[6] Michael R. Lyu,et al. Ratings meet reviews, a combined approach to recommend , 2014, RecSys '14.
[7] Scott Sanner,et al. AutoRec: Autoencoders Meet Collaborative Filtering , 2015, WWW.
[8] Chuhan Wu,et al. Hierarchical User and Item Representation with Three-Tier Attention for Recommendation , 2019, NAACL.
[9] Yoon Kim,et al. Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.
[10] Greg Linden,et al. Amazon . com Recommendations Item-to-Item Collaborative Filtering , 2001 .
[11] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] Xing Xie,et al. Neural Review Rating Prediction with Hierarchical Attentions and Latent Factors , 2019, DASFAA.
[13] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[14] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[15] Jeffrey Dean,et al. Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.
[16] Lei Zheng,et al. Joint Deep Modeling of Users and Items Using Reviews for Recommendation , 2017, WSDM.
[17] Alexander J. Smola,et al. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) , 2014, KDD.
[18] Steffen Rendle,et al. Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.
[19] Heng-Tze Cheng,et al. Wide & Deep Learning for Recommender Systems , 2016, DLRS@RecSys.
[20] Shuai Li,et al. Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed , 2019, IEEE Transactions on Industrial Informatics.
[21] Guokun Lai,et al. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis , 2014, SIGIR.
[22] John Riedl,et al. Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.
[23] Shuai Li,et al. Design and Analysis of FTZNN Applied to the Real-Time Solution of a Nonstationary Lyapunov Equation and Tracking Control of a Wheeled Mobile Manipulator , 2018, IEEE Transactions on Industrial Informatics.
[24] MengChu Zhou,et al. An Incremental-and-Static-Combined Scheme for Matrix-Factorization-Based Collaborative Filtering , 2016, IEEE Transactions on Automation Science and Engineering.
[25] Geoffrey E. Hinton,et al. Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.
[26] Jure Leskovec,et al. Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.
[27] Shafiq R. Joty,et al. ANR: Aspect-based Neural Recommender , 2018, CIKM.
[28] William W. Cohen,et al. TransNets: Learning to Transform for Recommendation , 2017, RecSys.
[29] Yehuda Koren,et al. Matrix Factorization Techniques for Recommender Systems , 2009, Computer.
[30] Dan Frankowski,et al. Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.
[31] Xing Xie,et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems , 2018, KDD.
[32] Tat-Seng Chua,et al. Neural Collaborative Filtering , 2017, WWW.
[33] MengChu Zhou,et al. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[34] Barry Smyth,et al. Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews , 2018, WWW.
[35] Siu Cheung Hui,et al. Multi-Pointer Co-Attention Networks for Recommendation , 2018, KDD.
[36] Ruslan Salakhutdinov,et al. Probabilistic Matrix Factorization , 2007, NIPS.
[37] MengChu Zhou,et al. Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data , 2018, IEEE Transactions on Cybernetics.
[38] Yiqun Liu,et al. Neural Attentional Rating Regression with Review-level Explanations , 2018, WWW.
[39] MengChu Zhou,et al. Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications , 2020, IEEE Transactions on Cybernetics.
[40] Jing Huang,et al. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction , 2017, RecSys.
[41] Donghyun Kim,et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation , 2016, RecSys.