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Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions

Yang Xiang, Li Fan, Chenke Yin, Menglin Kong, Chengtao Ji

TL;DR

This work tackles cold-start recommendations by quantifying how efficiently training knowledge is used through epistemic uncertainty. It introduces CREU, a three-part framework that (i) ensembles Conditional Variational AutoEncoders to warm item embeddings using side information, (ii) employs Pairwise-Distance Estimators with Sinkhorn divergence to measure and minimize epistemic uncertainty, and (iii) uses a backbone CTR predictor to fuse warmed embeddings with features. The approach yields improved cold-start and warm-start CTR performance on public datasets, supported by ablations showing the value of ensemble diversity and uncertainty-driven optimization. The method offers a principled way to maximize knowledge utilization in data-sparse recommendations and provides code for reproducibility.

Abstract

Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly focus on acquiring more knowledge to enrich embeddings or model inputs. However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty to indirectly define how efficiently a model uses the training knowledge. Since epistemic uncertainty represents the reducible part of the total uncertainty, we can optimize the recommendation model further based on epistemic uncertainty to improve its performance. To this end, we propose a Cold-Start Recommendation based on Epistemic Uncertainty (CREU) framework. Additionally, CREU is inspired by Pairwise-Distance Estimators (PaiDEs) to efficiently and accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The proposed method is evaluated through extensive offline experiments on public datasets, which further demonstrate the advantages and robustness of CREU. The source code is available at https://github.com/EsiksonX/CREU.

Harnessing Light for Cold-Start Recommendations: Leveraging Epistemic Uncertainty to Enhance Performance in User-Item Interactions

TL;DR

This work tackles cold-start recommendations by quantifying how efficiently training knowledge is used through epistemic uncertainty. It introduces CREU, a three-part framework that (i) ensembles Conditional Variational AutoEncoders to warm item embeddings using side information, (ii) employs Pairwise-Distance Estimators with Sinkhorn divergence to measure and minimize epistemic uncertainty, and (iii) uses a backbone CTR predictor to fuse warmed embeddings with features. The approach yields improved cold-start and warm-start CTR performance on public datasets, supported by ablations showing the value of ensemble diversity and uncertainty-driven optimization. The method offers a principled way to maximize knowledge utilization in data-sparse recommendations and provides code for reproducibility.

Abstract

Most recent paradigms of generative model-based recommendation still face challenges related to the cold-start problem. Existing models addressing cold item recommendations mainly focus on acquiring more knowledge to enrich embeddings or model inputs. However, many models do not assess the efficiency with which they utilize the available training knowledge, leading to the extraction of significant knowledge that is not fully used, thus limiting improvements in cold-start performance. To address this, we introduce the concept of epistemic uncertainty to indirectly define how efficiently a model uses the training knowledge. Since epistemic uncertainty represents the reducible part of the total uncertainty, we can optimize the recommendation model further based on epistemic uncertainty to improve its performance. To this end, we propose a Cold-Start Recommendation based on Epistemic Uncertainty (CREU) framework. Additionally, CREU is inspired by Pairwise-Distance Estimators (PaiDEs) to efficiently and accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The proposed method is evaluated through extensive offline experiments on public datasets, which further demonstrate the advantages and robustness of CREU. The source code is available at https://github.com/EsiksonX/CREU.

Paper Structure

This paper contains 13 sections, 11 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Methodology
  4. Ensembled CVAEs
  5. Epistemic Uncertainty-based Optimization
  6. CTR Prediction
  7. Experiments
  8. Experiments Setup
  9. Performance Comparison
  10. Ablation Study
  11. Effectiveness of Each Component
  12. Utilization Analysis of Training Knowledge through Epistemic Uncertainty
  13. Conclusion

Figures (2)

  • Figure 1: The architecture of the proposed CREU model.
  • Figure 2: Ablation study of CREU on the MovieLens-1M dataset. (1st row: DeepFM backbone, 2nd row: W&D backbone, Color bar: epistemic uncertainty.)