By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.
To achieve this, we firstly formalize sequential recommendation as a problem to estimate conditional probability given temporal dynamic heterogeneous graphs and user behavior sequences.
Existing leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e. g., operator, string, etc.
Joint extraction of entities and relations is an important task in information extraction.
Ranked #3 on Relation Extraction on NYT-single