DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops

• URL: http://arxiv.org/abs/2311.05864v1
• Date: Fri, 10 Nov 2023 04:36:00 GMT
• Title: DPR: An Algorithm Mitigate Bias Accumulation in Recommendation feedback loops
• Authors: Hangtong Xu and Yuanbo Xu and Yongjian Yang and Fuzhen Zhuang and Hui Xiong
• Abstract summary: We study the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience. We propose Dynamic Personalized Ranking (textbfDPR), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects. We show theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms.
• Score: 41.21024436158042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation models trained on the user feedback collected from deployed recommendation systems are commonly biased. User feedback is considerably affected by the exposure mechanism, as users only provide feedback on the items exposed to them and passively ignore the unexposed items, thus producing numerous false negative samples. Inevitably, biases caused by such user feedback are inherited by new models and amplified via feedback loops. Moreover, the presence of false negative samples makes negative sampling difficult and introduces spurious information in the user preference modeling process of the model. Recent work has investigated the negative impact of feedback loops and unknown exposure mechanisms on recommendation quality and user experience, essentially treating them as independent factors and ignoring their cross-effects. To address these issues, we deeply analyze the data exposure mechanism from the perspective of data iteration and feedback loops with the Missing Not At Random (\textbf{MNAR}) assumption, theoretically demonstrating the existence of an available stabilization factor in the transformation of the exposure mechanism under the feedback loops. We further propose Dynamic Personalized Ranking (\textbf{DPR}), an unbiased algorithm that uses dynamic re-weighting to mitigate the cross-effects of exposure mechanisms and feedback loops without additional information. Furthermore, we design a plugin named Universal Anti-False Negative (\textbf{UFN}) to mitigate the negative impact of the false negative problem. We demonstrate theoretically that our approach mitigates the negative effects of feedback loops and unknown exposure mechanisms. Experimental results on real-world datasets demonstrate that models using DPR can better handle bias accumulation and the universality of UFN in mainstream loss methods.

Related papers

• Negative Sampling in Recommendation: A Survey and Future Directions [43.11318243903388]
  Negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors. We conduct an extensive literature review on the existing negative sampling strategies in recommendation. We detail the insights of the tailored negative sampling strategies in diverse recommendation scenarios.
  arXiv  Detail & Related papers  (2024-09-11T12:48:52Z)
• Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation [67.88747330066049]
  Fine-grained feedback captures nuanced distinctions in image quality and prompt-alignment. We show that demonstrating its superiority to coarse-grained feedback is not automatic. We identify key challenges in eliciting and utilizing fine-grained feedback.
  arXiv  Detail & Related papers  (2024-06-24T17:19:34Z)
• Source Echo Chamber: Exploring the Escalation of Source Bias in User, Data, and Recommender System Feedback Loop [65.23044868332693]
  We investigate the impact of source bias on the realm of recommender systems. We show the prevalence of source bias and reveal a potential digital echo chamber with source bias amplification. We introduce a black-box debiasing method that maintains model impartiality towards both HGC and AIGC.
  arXiv  Detail & Related papers  (2024-05-28T09:34:50Z)
• BHEISR: Nudging from Bias to Balance -- Promoting Belief Harmony by Eliminating Ideological Segregation in Knowledge-based Recommendations [5.795636579831129]
  The main objective is to strike a belief balance for users while minimizing the detrimental influence caused by filter bubbles. The BHEISR model amalgamates principles from nudge theory while upholding democratic and transparent principles.
  arXiv  Detail & Related papers  (2023-07-06T06:12:37Z)
• Generating Negative Samples for Sequential Recommendation [83.60655196391855]
  We propose to Generate Negative Samples (items) for Sequential Recommendation (SR) A negative item is sampled at each time step based on the current SR model's learned user preferences toward items. Experiments on four public datasets verify the importance of providing high-quality negative samples for SR.
  arXiv  Detail & Related papers  (2022-08-07T05:44:13Z)
• 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)
• Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction [14.462884375151045]
  Delayed feedback is crucial for the conversion rate prediction in online advertising. Previous delayed feedback modeling methods balance the trade-off between waiting for accurate labels and consuming fresh feedback. We propose a new method, DElayed Feedback modeling with UnbiaSed Estimation, (DEFUSE), which aim to respectively correct the importance weights of the immediate positive, the fake negative, the real negative, and the delay positive samples.
  arXiv  Detail & Related papers  (2022-02-14T03:31:09Z)
• Existence conditions for hidden feedback loops in online recommender systems [0.0]
  We study how uncertainty and noise in user interests influence the existence of feedback loops. A non-zero probability of resetting user interests is sufficient to limit the feedback loop and estimate the size of the effect.
  arXiv  Detail & Related papers  (2021-09-11T13:30:08Z)
• Probabilistic and Variational Recommendation Denoising [56.879165033014026]
  Learning from implicit feedback is one of the most common cases in the application of recommender systems. We propose probabilistic and variational recommendation denoising for implicit feedback. We employ the proposed DPI and DVAE on four state-of-the-art recommendation models and conduct experiments on three datasets.
  arXiv  Detail & Related papers  (2021-05-20T08:59:44Z)

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.