This document summarizes context-aware recommendation and factorization machines. It discusses how factorization machines improve on traditional matrix factorization models by incorporating additional context features. It also introduces gradient boosting factorization machines which further enhance factorization machines by optimizing the factorization model with gradient boosting algorithms.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.
This document summarizes context-aware recommendation and factorization machines. It discusses how factorization machines improve on traditional matrix factorization models by incorporating additional context features. It also introduces gradient boosting factorization machines which further enhance factorization machines by optimizing the factorization model with gradient boosting algorithms.
This document summarizes research on using structured event representations extracted from news articles to predict stock price movements. Key points include:
- Events are extracted from articles and represented as tuples of actors, actions, and objects to capture the who, what, when of events.
- A deep neural network model is used to predict stock price changes based on extracted event representations.
- The model achieves better performance than baselines that use bag-of-words representations of articles.