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Found 8 papers, 1 papers with code
Generating and Modifying Natural Language Explanations
HESIP is a hybrid explanation system for image predictions that combines sub-symbolic and symbolic machine learning techniques to explain the predictions of image classification tasks.
A Deep Framework for Cross-Domain and Cross-System Recommendations
Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.
Graph Learning Approaches to Recommender Systems: A Review
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
Enabling the Analysis of Personality Aspects in Recommender Systems
Existing Recommender Systems mainly focus on exploiting users' feedback, e. g., ratings, and reviews on common items to detect similar users.
Sequential Recommender Systems: Challenges, Progress and Prospects
The emerging topic of sequential recommender systems has attracted increasing attention in recent years. Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time.
On Conforming and Conflicting Values
They are distinguished by the fact that the latter in some sense can be thought of as being independent of actions.
A Survey on Session-based Recommender Systems
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.
