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Found 11 papers, 7 papers with code
Rectifying Demonstration Shortcut in In-Context Learning
However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction.
Deep Rating Elicitation for New Users in Collaborative Filtering
Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations.
Confidence Calibration for Recommender Systems and Its Applications
Despite the importance of having a measure of confidence in recommendation results, it has been surprisingly overlooked in the literature compared to the accuracy of the recommendation.
Top-Personalized-K Recommendation
To address this issue, we introduce Top-Personalized-K Recommendation, a new recommendation task aimed at generating a personalized-sized ranking list to maximize individual user satisfaction.
Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
On this basis, we propose a Doubly Calibrated Estimator that involves the calibration of both the imputation and propensity models.
Distillation from Heterogeneous Models for Top-K Recommendation
Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), to reduce the huge inference costs while retaining high accuracy.
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering
ConCF constructs a multi-branch variant of a given target model by adding auxiliary heads, each of which is trained with heterogeneous objectives.
Obtaining Calibrated Probabilities with Personalized Ranking Models
Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.
Topology Distillation for Recommender System
To address this issue, we propose a novel method named Hierarchical Topology Distillation (HTD) which distills the topology hierarchically to cope with the large capacity gap.
Bidirectional Distillation for Top-K Recommender System
Recommender systems (RS) have started to employ knowledge distillation, which is a model compression technique training a compact model (student) with the knowledge transferred from a cumbersome model (teacher).
DE-RRD: A Knowledge Distillation Framework for Recommender System
Recent recommender systems have started to employ knowledge distillation, which is a model compression technique distilling knowledge from a cumbersome model (teacher) to a compact model (student), to reduce inference latency while maintaining performance.
