Corpus ID: 53987113

Dessert Steak Seafood Vegetable Bread Fruit Meat Food Oyster Shrimp Gelato Cake Food High Fiber Food Croissant

  title={Dessert Steak Seafood Vegetable Bread Fruit Meat Food Oyster Shrimp Gelato Cake Food High Fiber Food Croissant},
  author={Jingyue Gao and Xiting Wang and Yasha Wang and Xing Xie},
Recommender systems have been playing an increasingly important role in our daily life due to the explosive growth of information. Accuracy and explainability are two core aspects when we evaluate a recommendation model and have become one of the fundamental trade-offs in machine learning. In this paper, we propose to alleviate the trade-off between accuracy and explainability by developing an explainable deep model that combines the advantages of deep learning-based models and existing… Expand

Figures and Tables from this paper


TEM: Tree-enhanced Embedding Model for Explainable Recommendation
A novel solution named Tree-enhanced Embedding Method that combines the strengths of embedding-based and tree-based models, and at the core of the embedding method is an easy-to-interpret attention network, making the recommendation process fully transparent and explainable. Expand
GRAM: Graph-based Attention Model for Healthcare Representation Learning
Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Expand
Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems
This work proposes a novel approach for extracting explanations from latent factor recommendation systems by training association rules on the output of a matrix factorisation black-box model, which mitigates the accuracy-interpretability trade-off whilst avoiding the need to sacrifice flexibility or use external data sources. Expand
Collaborative Knowledge Base Embedding for Recommender Systems
A heterogeneous network embedding method is adopted, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships and a final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering. Expand
A Reinforcement Learning Framework for Explainable Recommendation
A reinforcement learning framework for explainable recommendation that can explain any recommendation model (model-agnostic) and can flexibly control the explanation quality based on the application scenario is designed. Expand
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis
The Explicit Factor Model (EFM) is proposed to generate explainable recommendations, meanwhile keep a high prediction accuracy, and online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior. Expand
Taxonomy discovery for personalized recommendation
A novel approach that automatically discovers the taxonomies from online shopping data and jointly learns a taxonomy-based recommendation system that outperforms latent factor models based on the human-induced taxonomy, thus alleviating the need for costly manual taxonomy generation. Expand
Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior
This paper proposes a taxonomy-aware latent factor model (TF) which combines taxonomies and latent factors using additive models and develops efficient algorithms to train the TF models, which scales to large number of users/items and develops scalable inference/recommendation algorithms by exploiting the structure of the taxonomy. Expand
Collaborative Deep Learning for Recommender Systems
A hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix is proposed, which can significantly advance the state of the art. Expand
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A deep model to learn item properties and user behaviors jointly from review text, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. Expand