Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation

@article{Boratto2020ConnectingUA,
  title={Connecting User and Item Perspectives in Popularity Debiasing for Collaborative Recommendation},
  author={Ludovico Boratto and Gianni Fenu and Mirko Marras},
  journal={Inf. Process. Manag.},
  year={2020},
  volume={58},
  pages={102387}
}
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45 Citations
Highly Influential Citations
6
Background Citations
22
Methods Citations
5
Results Citations
2

45 Citations

Novel approaches to measuring the popularity inclination of users for the popularity bias problem

This study proposes two novel approaches, namely Better-Than-Average (BTA) and Positively-Rated (PR), to measure the popularity inclination of individuals more correctly, which aim to consider only items favored by users rather than all rated ones but differ in how they select the set of liked items.

Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering

Considering the theoretical guarantee and empirical success of BC loss, this work advocates using it not just as a debiasing strategy, but also as a standard loss in recommender models.

Exploring potential biases towards blockbuster items in ranking-based recommendations

  • Emre Yalcin
  • Computer Science
    Data Mining and Knowledge Discovery
  • 2022
This study presents a practical formulation that measures the degree of the blockbuster level of the items by combining their liking-degree and popularity effectively, and performs a comprehensive set of experiments to explore the potential biases towards blockbuster items in recommendations.

Countering Popularity Bias by Regularizing Score Differences

This study proposes a novel method to reduce the model bias while maintaining accuracy by directly regularizing the recommendation scores to be equal across items a user preferred by extending the widely used pairwise loss which maximizes the score differences between preferred and unpreferred items.

A Formal Analysis of Recommendation Quality of Adversarially-trained Recommenders

This work model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items.

Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality

This work model the learning characteristics of the BPR and APR optimization frameworks to give mathematical evidence that, when the feedback data have a tailed distribution, APR amplifies the popularity bias more than BPR due to an unbalanced number of received positive updates from short-head items.

MhSa-GRU: combining user’s dynamic preferences and items’ correlation to augment sequence recommendation

The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation

This work proposes an Interaction Graph Auto-encoder Network (IGA) based on topology-aware to address the transferable recommendation problem and demonstrates that IGA outperforms state-of-the-art approaches in transferable scenarios.

Evaluating unfairness of popularity bias in recommender systems: A comprehensive user-centric analysis

Self-Supervised learning for Conversational Recommendation

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