How Fair is Your Diffusion Recommender Model? (2409.04339v1)

Abstract: Diffusion-based recommender systems have recently proven to outperform traditional generative recommendation approaches, such as variational autoencoders and generative adversarial networks. Nevertheless, the machine learning literature has raised several concerns regarding the possibility that diffusion models, while learning the distribution of data samples, may inadvertently carry information bias and lead to unfair outcomes. In light of this aspect, and considering the relevance that fairness has held in recommendations over the last few decades, we conduct one of the first fairness investigations in the literature on DiffRec, a pioneer approach in diffusion-based recommendation. First, we propose an experimental setting involving DiffRec (and its variant L-DiffRec) along with nine state-of-the-art recommendation models, two popular recommendation datasets from the fairness-aware literature, and six metrics accounting for accuracy and consumer/provider fairness. Then, we perform a twofold analysis, one assessing models' performance under accuracy and recommendation fairness separately, and the other identifying if and to what extent such metrics can strike a performance trade-off. Experimental results from both studies confirm the initial unfairness warnings but pave the way for how to address them in future research directions.

• The paper demonstrates that diffusion-based recommender systems can achieve competitive accuracy while compromising on both consumer and provider fairness.
• It employs a two-stage evaluation framework using metrics like Recall, nDCG, ∆Recall, and ∆nDCG to rigorously compare DiffRec and L-DiffRec with traditional models.
• The findings highlight L-DiffRec's potential with clustering strategies to mitigate bias, paving the way for more equitable recommendation systems.

Fairness in Diffusion-based Recommender Systems: An Examination of DiffRec and L-DiffRec

The paper "How Fair is Your Diffusion Recommender Model?" by Daniele Malitesta et al., fundamentally addresses fairness issues in diffusion-based recommender systems (RSs), with a particular focus on DiffRec and its variant L-DiffRec. This research is particularly timely, given the burgeoning interest in training diffusion models for various applications while ensuring equitable outcomes.

Overview

Diffusion models have garnered popularity in generative AI, outperforming traditional generative models like variational autoencoders (VAEs) and generative adversarial networks (GANs). However, concerns about bias and fairness in these models are growing. The paper presents an extensive fairness evaluation framework for DiffRec and L-DiffRec by contrasting their performance with nine state-of-the-art recommender systems across two well-known datasets: MovieLens-1M (ML1M) and Foursquare Tokyo (FTKY).

Methodology

The authors implemented a two-stage experimental analysis. Initially, they evaluated the models based on individual metrics of recommendation accuracy (Recall and nDCG), consumer fairness (∆Recall and ∆nDCG), and provider fairness (APLT and ∆Exp). Subsequently, they performed a trade-off analysis to assess the models' ability to balance accuracy and fairness. This robust experimental design ensures comprehensive insights into both absolute and comparative performances of the models.

Numerical Results

The paper provided a multifaceted analysis of the performance of DiffRec and L-DiffRec:

1. Accuracy: DiffRec demonstrated competitive performance, often surpassing traditional models like BPRMF, ItemKNN, and NeuMF. However, its accuracy came at the cost of fairness.
2. Consumer Fairness: Measures like ∆Recall and ∆nDCG indicated the presence of bias in DiffRec and L-DiffRec. Particularly on the ML1M dataset, DiffRec showed significant consumer unfairness, while L-DiffRec exhibited relatively better, though still suboptimal, fairness attributes.
3. Provider Fairness: The results for provider fairness are also concerning. DiffRec's negative impact on the diversity of recommended items underscores the necessity for incorporating fairness-focused mechanisms. Intriguingly, L-DiffRec’s clustering and compression strategy led to more balanced outcomes, indicating potential pathways for mitigating bias.

Implications and Future Work

The implications of this research are significant for both the RS and broader AI communities:

• Theoretical: The paper highlights that diffusion models, despite their effectiveness, are prone to perpetuate and even exacerbate biases present in training data. This underscores the critical need for fairness-aware designs in the development of these models.
• Practical: From a deployment perspective, adopting strategies evident in L-DiffRec such as latent space clustering and item-specific modeling can mitigate fairness concerns. This holds promise for real-world applications where fairness is paramount, such as personalized content delivery and recommendation in diverse demographic settings.

In terms of future developments, the paper suggests extending the analysis to other diffusion-based RS models like T-DiffRec and exploring alternative fairness-enhancing techniques. Additionally, exploring methodologies for dynamically adjusting fairness constraints during the recommendation process could offer further insights into achieving both high accuracy and fairness.

Conclusion

This paper provides a critical examination of fairness in diffusion-based recommender systems, particularly DiffRec. The findings emphasize the importance of addressing inherent biases within these models, proposing that variant strategies such as those in L-DiffRec show promise in balancing accuracy and fairness. The research paves the way for future investigations aimed at enhancing fairness in RSs, ensuring that the benefits of advanced AI models are equitably distributed.

This concise and technical summary captures the essence of the paper while adhering to the academic tone and format suitable for a knowledgeable audience. The analysis not only underscores the importance of fairness in RSs but also sheds light on future research directions to improve upon existing models.