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PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation
The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups.
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions
We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.
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