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Found 5 papers, 4 papers with code
Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
In this paper, we propose a new perspective for context-aware users' behavior modeling by including the whole page-wisely exposed products and the corresponding feedback as contextualized page-wise feedback sequence.
Knowledge-enhanced Session-based Recommendation with Temporal Transformer
We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer).
Neural Interactive Collaborative Filtering
Therefore, the proposed exploration policy, to balance between learning the user profile and making accurate recommendations, can be directly optimized by maximizing users' long-term satisfaction with reinforcement learning.
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts.
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Instantiated rules contain constants extracted from KGs.
