Causal Intervention for Fairness in Multi-behavior Recommendation

• URL: http://arxiv.org/abs/2209.04589v3
• Date: Wed, 17 Apr 2024 13:16:02 GMT
• Title: Causal Intervention for Fairness in Multi-behavior Recommendation
• Authors: Xi Wang, Wenjie Wang, Fuli Feng, Wenge Rong, Chuantao Yin, Zhang Xiong,
• Abstract summary: We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality. To handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors.
• Score: 40.938727601434195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems usually learn user interests from various user behaviors, including clicks and post-click behaviors (e.g., like and favorite). However, these behaviors inevitably exhibit popularity bias, leading to some unfairness issues: 1) for items with similar quality, more popular ones get more exposure; and 2) even worse the popular items with lower popularity might receive more exposure. Existing work on mitigating popularity bias blindly eliminates the bias and usually ignores the effect of item quality. We argue that the relationships between different user behaviors (e.g., conversion rate) actually reflect the item quality. Therefore, to handle the unfairness issues, we propose to mitigate the popularity bias by considering multiple user behaviors. In this work, we examine causal relationships behind the interaction generation procedure in multi-behavior recommendation. Specifically, we find that: 1) item popularity is a confounder between the exposed items and users' post-click interactions, leading to the first unfairness; and 2) some hidden confounders (e.g., the reputation of item producers) affect both item popularity and quality, resulting in the second unfairness. To alleviate these confounding issues, we propose a causal framework to estimate the causal effect, which leverages backdoor adjustment to block the backdoor paths caused by the confounders. In the inference stage, we remove the negative effect of popularity and utilize the good effect of quality for recommendation. Experiments on two real-world datasets validate the effectiveness of our proposed framework, which enhances fairness without sacrificing recommendation accuracy.

Related papers

• Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence [54.13541697801396]
  We propose a new task named Proactive Recommendation in Social Networks (PRSN) PRSN indirectly steers users' interest by utilizing the influence of social neighbors. We propose a Neighbor Interference Recommendation (NIRec) framework with two key modules.
  arXiv  Detail & Related papers  (2024-09-13T15:53:40Z)
• Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias [34.006766098392525]
  Collaborative Filtering (CF) typically suffers from the challenge of popularity bias due to the uneven distribution of items in real-world datasets. This bias leads to a significant accuracy gap between popular and unpopular items. We propose Popularity-Aware Alignment and Contrast (PAAC) to address two challenges.
  arXiv  Detail & Related papers  (2024-05-31T09:14:48Z)
• A First Look at Selection Bias in Preference Elicitation for Recommendation [64.44255178199846]
  We study the effect of selection bias in preference elicitation on the resulting recommendations. A big hurdle is the lack of any publicly available dataset that has preference elicitation interactions. We propose a simulation of a topic-based preference elicitation process.
  arXiv  Detail & Related papers  (2024-05-01T14:56:56Z)
• Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems [74.47680026838128]
  Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias. We consider multifactorial selection bias affected by both item and rating value factors. We propose smoothing and alternating gradient descent techniques to reduce variance and improve the robustness of its optimization.
  arXiv  Detail & Related papers  (2024-04-29T12:18:21Z)
• Test Time Embedding Normalization for Popularity Bias Mitigation [6.145760252113906]
  Popularity bias is a widespread problem in the field of recommender systems. We propose 'Test Time Embedding Normalization' as a simple yet effective strategy for mitigating popularity bias.
  arXiv  Detail & Related papers  (2023-08-22T08:57:44Z)
• Ranking with Popularity Bias: User Welfare under Self-Amplification Dynamics [19.59766711993837]
  We propose and theoretically analyze a general mechanism by which item popularity, item quality, and position bias jointly impact user choice. We show that naive popularity-biased recommenders induce linear regret by conflating item quality and popularity.
  arXiv  Detail & Related papers  (2023-05-24T22:38:19Z)
• The Bandwagon Effect: Not Just Another Bias [13.579420996461439]
  We argue that the bandwagon effect should not be seen as a problem of statistical bias. We show that it can make estimators inconsistent, introducing a distinct set of problems for convergence in relevance estimation. This work aims to show that the bandwagon effect poses an underinvestigated open problem that is fundamentally distinct from the well-studied selection bias in recommendation.
  arXiv  Detail & Related papers  (2022-06-25T17:24:35Z)
• Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets [62.146882023375746]
  We propose a model of an idealized online cultural market in which $N$ items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items' popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings.
  arXiv  Detail & Related papers  (2022-04-28T14:36:11Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• User-centered Evaluation of Popularity Bias in Recommender Systems [4.30484058393522]
  Recommendation and ranking systems suffer from popularity bias; the tendency of the algorithm to favor a few popular items while under-representing the majority of other items. In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' perspective. We present an effective approach that mitigates popularity bias from the user-centered point of view.
  arXiv  Detail & Related papers  (2021-03-10T22:12:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.