Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences (2409.16478v1)

Abstract: Digital platforms such as social media and e-commerce websites adopt Recommender Systems to provide value to the user. However, the social consequences deriving from their adoption are still unclear. Many scholars argue that recommenders may lead to detrimental effects, such as bias-amplification deriving from the feedback loop between algorithmic suggestions and users' choices. Nonetheless, the extent to which recommenders influence changes in users leaning remains uncertain. In this context, it is important to provide a controlled environment for evaluating the recommendation algorithm before deployment. To address this, we propose a stochastic simulation framework that mimics user-recommender system interactions in a long-term scenario. In particular, we simulate the user choices by formalizing a user model, which comprises behavioral aspects, such as the user resistance towards the recommendation algorithm and their inertia in relying on the received suggestions. Additionally, we introduce two novel metrics for quantifying the algorithm's impact on user preferences, specifically in terms of drift over time. We conduct an extensive evaluation on multiple synthetic datasets, aiming at testing the robustness of our framework when considering different scenarios and hyper-parameters setting. The experimental results prove that the proposed methodology is effective in detecting and quantifying the drift over the users preferences by means of the simulation. All the code and data used to perform the experiments are publicly available.

Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences

The paper, "Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences," introduces a simulation framework designed to examine the long-term interactions between users and recommendation algorithms. Authored by researchers from various institutions, the paper focuses on understanding how recommender systems impact user behaviors over time, specifically considering phenomena such as bias amplification and opinion drift.

Introduction and Motivation

Recommender systems are ubiquitous in digital platforms, serving as essential tools for sifting through voluminous content. Despite their utility, such algorithms have been scrutinized for potential adverse effects, including echo chambers and polarization. Quantifying the extent of influence that recommender systems have on user preferences remains an elusive task. The authors propose a stochastic simulation model that can analyze these interactions in a controlled, long-term scenario. This framework aims to detect and quantify algorithmic drift — the shift in user preferences prompted by engagement with these systems.

Simulation Framework

The core of the proposed framework is a user-recommender interaction model defined as follows:

1. User Modeling: Users are characterized based on their resistance to recommendations and inertia in accepting algorithmic suggestions. This behavioral model acknowledges that users may autonomously choose items from the catalog or adhere strictly to algorithmic recommendations.
2. Recommendation Process: The recommender system generates a list of items ranked by relevance. Users interact with these lists over multiple iterations, influencing their subsequent recommendations and deriving a feedback loop.
3. Evaluation Metrics: Two novel metrics are introduced:
   • Algorithmic Drift Score (ADS): A graph-based measure determining the deviation in user preferences, borrowed from Random Walk Controversy Score (RWC).
   • Delta Target Consumption (DTC): This measures the change in the proportion of interactions with specific categories (e.g., harmful or neutral content) before and after the simulation.

Methodology

The simulation iteratively updates user histories based on their interactions, mimicking realistic engagement with the recommender system. The experimental setup includes varying the user's resistance and inertia to examine different behavioral patterns. The framework's robustness is evaluated using synthetic datasets that categorize users into non-radicalized, semi-radicalized, and radicalized based on their interaction with harmful versus neutral content.

Experimental Results

Population Proportion Impact: By varying the proportion of non-/semi-/radicalized users within the dataset, the authors demonstrate that both ADS and DTC are effective in detecting significant algorithmic drift. Specifically, samples with higher portions of semi-radicalized users exhibit more pronounced shifts in user preferences due to the recommender system’s influence.

Behavioral Parameters: The experiments were extended to assess the impact of resistance and inertia parameters on user preference shifts. Low resistance and high inertia resulted in more significant drifts, whereas high resistance or low inertia mitigated this effect. These findings align with intuitive expectations regarding user engagement with recommendations.

Randomness Effects: Introducing randomness in user choices (e.g., influenced by external factors such as mismclicks or peer recommendations) showed minimal long-term impact on user preferences, highlighting the robustness of user behavior quantification against stochastic noise.

Implications and Future Work

This research contributes a robust methodology for scrutinizing the effects of recommendation systems before their deployment. The implications are significant for platforms aiming to mitigate potential drifts that could lead to undesirable social phenomena, such as polarization or radicalization.

Practical Implications: Platforms can leverage the proposed framework to comprehensively evaluate their recommendation algorithms, minimizing adverse effects before live deployment. It can serve as a critical tool for responsible AI development and deployment strategies.

Theoretical Implications: The paper advances our understanding of feedback loops in recommender systems, reinforcing the importance of considering long-term interactions rather than short-term optimization. It underscores the necessity for dynamic models that can adapt to evolving user preferences.

Future Directions:

• Dynamic Content: Future research could explore dynamic item sets where new content continuously integrates into the platform.
• Contextualization: Extending the user model to consider contextual factors and user-specific conditions, foregrounding context-aware recommendation frameworks.
• Bias and Diversity: Investigating how this framework can measure and mitigate other biases, such as popularity bias, and enhance diversity and serendipity in recommendations.

In conclusion, the paper offers substantial progress in understanding and measuring the long-term impacts of recommender systems on user behavior. As the complexity and reach of recommendation algorithms continue to grow, such frameworks are invaluable for promoting ethical and user-centric AI systems.