Incorporating Phrase-level Sentiment Analysis on Textual Reviews for Personalized Recommendation
By Yongfeng Zhang. 2015
83 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yongfeng Zhang
- Xu Chen
- Behnoush Abdollahi
- Olfa Nasraoui
- Filip Radlinski
- Krisztian Balog
- Shushan Arakelyan
- Roberto Confalonieri
In Information Systems
- Xu Chen
- Yongfeng Zhang
- Behnoush Abdollahi
- Olfa Nasraoui
- Filip Radlinski
- Krisztian Balog
- Shushan Arakelyan
- Roberto Confalonieri
Citations in the corpus
- Explainable Recommendation: A Survey and New Perspectives (2018, methodology): “To alleviate the problem, Zhang et al. [2014a] proposed Explicit Factor Models (EFM), where the basic idea is to recommend prod-”
- Sequential Recommendation with User Memory Networks (2018, unknown): “user textual reviews [2, 12, 33, 34]) for explanations.”
- Explainable Restricted Boltzmann Machines for Collaborative Filtering (2016, background): “They further expanded their feature-level explanation by considering different forms such as word clouds (Zhang, 2015).”
- A historical perspective of explainable Artificial Intelligence (2020, background): “Explicit factor models (EFM; Zhang, 2015) take into account information provided by the user about features of items that she might be interested in (through reviews and explicit feedback) and map them to the latent factors used in the (matrix or tensor) factorization part.”
- Explainable recommendation with fusion of aspect information (2018, methodology): “The existing methods for explainable recommendation [1, 21, 42] often suffer from two defects.”
- Improving the Interpretability of Neural Sentiment Classifiers via Data Augmentation (2019, background): “classification, prior work on recommendation systems with sentiment analysis shows that recommendations with explanations have more influential impact on users behavior (Zhang et al., 2014; Zhang, 2015).”
- Transparent, Scrutable and Explainable User Models for Personalized Recommendation (2019, background): “Various forms of explanations have been explored in prior work, including sentences [21, 55], tag/keyword clouds [17, 30, 53], as well as different kinds of visualizations [10, 22, 31, 47].”