User Modeling with Neural Network for Review Rating Prediction

By Duyu Tang, Bing Qin, Ting-Wei Liu, Yuekui Yang. 2015

161 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qiang Yang
2. Yu Zhang
3. Mohit Bansal
4. Jiwei Li
5. Guangneng Hu
6. Xiangnan He
7. Dan Jurafsky
8. Jianfeng Gao

Citations in the corpus

• Deep Neural Networks for YouTube Recommendations (2016, methodology): “Neural networks are used for recommending news in [17], citations in [8] and review ratings in [20].”
• Towards Explainable NLP: A Generative Explanation Framework for Text Classification (2018, methodology): “Unfortunately, existing text classification models typically utilize the original review texts to predict the overall results (Tang et al., 2015b; Asghar, 2016; Xu et al., 2017) directly, but none of them provides fine-grained explanations about why the decisions are made.”
• A survey on classification techniques for opinion mining and sentiment analysis (2017, methodology): “Opinion mining, can take place in three levels of the document (Sharma et al. 2014; Moraes et al. 2013; Tang et al. 2015; Sun et al. 2015; Xia et al. 2016), sentence (Marcheggiani et al.”
• Understanding Neural Networks through Representation Erasure (2016, methodology): “Task, Dataset and Training Inspired by recent visualization work from Lei et al. (2016), we focus on the task of document-level aspect rating prediction (Tang et al., 2015a; Tang et al., 2015b).”
• Learning Semantic Representations of Users and Products for Document Level Sentiment Classification (2015, background): “This work is an extension of our previous work (Tang et al., 2015), which only takes consideration of userword association.”
• Social Collaborative Viewpoint Regression with Explainable Recommendations (2017, background): “[42] present a user-vector composition model to predict user ratings.”
• A review on deep learning for recommender systems: challenges and remedies (2018, background): “…et al. (2014) Huang et al. (2015) Paradarami et al. (2017) Zheng et al. (2016a) Kyo-Joong et al. (2014) Yang et al. (2017) Du et al. (2017) Tang et al. (2015) Lian et al. (2017) Barbieri et al. (2017) Suglia et al. (2017) Li and She (2017) Cao et al. (2017) Oramas et al. (2017) Kumar et…”
• Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018, methodology): “The NLP approach was applied to personalized modeling of reviews [40] (with a slightly different mathematical structure).”
• Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text (2019, background): “rather than the pre-trained word embeddings themselves. The reason is as follows. Though some sentimental words like ‘good’ and ‘bad’ are somewhat important, they also depend on who wrote the reviews [47]. Some people are critical and hence when they give a ‘good’ word in the reivew, it means that t”