Search Results for author:
Found 3 papers, 2 papers with code
Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systems
Detecting beneficial feature interactions is essential in recommender systems, and existing approaches achieve this by examining all the possible feature interactions.
Neural Graph Matching based Collaborative Filtering
User and item attributes are essential side-information; their interactions (i. e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems.
Learning Based Distributed Tracking
Inspired by the great success of machine learning in the past decade, people have been thinking about the possibility of improving the theoretical results by exploring data distribution.
Data Structures and Algorithms
