Publications by authors named "Fengzhu Wu"
TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations.
IEEE Trans Neural Netw Learn Syst
February 2024
Article Synopsis
- Sequential recommendation seeks to suggest the best items for a user at a specific time based on their past behavior, but traditional methods often overlook how users influence each other.
- The proposed framework incorporates dynamic user-item heterogeneous graphs to effectively model both user behaviors and inter-user influences, enhancing the recommendation process.
- By using conditional probability estimation and conditional random fields for aggregating data, the method shows promising results on various datasets, highlighting its effectiveness in sequential recommendations.
Chronic heart failure (CHF) and diabetes mellitus are associated with morbidity and mortality. CHF and diabetes generally simultaneously occur, resulting in adverse outcomes. Diabetes complicates cardiomyopathy and exacerbates heart failure conditions.
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