When Online Algorithms Influence the Environment: A Dynamical Systems Analysis of the Unintended Consequences

• URL: http://arxiv.org/abs/2411.13883v1
• Date: Thu, 21 Nov 2024 06:47:53 GMT
• Title: When Online Algorithms Influence the Environment: A Dynamical Systems Analysis of the Unintended Consequences
• Authors: Prabhat Lankireddy, Jayakrishnan Nair, D Manjunath,
• Abstract summary: We analyze the effect that online algorithms have on the environment that they are learning. We show that when the recommendation algorithm is able to learn the population preferences in the presence of this mismatch, the algorithm induces similarity in the preferences of the user population.
• Score: 5.4209739979186295
• License:
• Abstract: We analyze the effect that online algorithms have on the environment that they are learning. As a motivation, consider recommendation systems that use online algorithms to learn optimal product recommendations based on user and product attributes. It is well known that the sequence of recommendations affects user preferences. However, typical learning algorithms treat the user attributes as static and disregard the impact of their recommendations on user preferences. Our interest is to analyze the effect of this mismatch between the model assumption of a static environment, and the reality of an evolving environment affected by the recommendations. To perform this analysis, we first introduce a model for a generic coupled evolution of the parameters that are being learned, and the environment that is affected by it. We then frame a linear bandit recommendation system (RS) into this generic model where the users are characterized by a state variable that evolves based on the sequence of recommendations. The learning algorithm of the RS does not explicitly account for this evolution and assumes that the users are static. A dynamical system model that captures the coupled evolution of the population state and the learning algorithm is described, and its equilibrium behavior is analyzed. We show that when the recommendation algorithm is able to learn the population preferences in the presence of this mismatch, the algorithm induces similarity in the preferences of the user population. In particular, we present results on how different properties of the recommendation algorithm, namely the user attribute space and the exploration-exploitation tradeoff, effect the population preferences when they are learned by the algorithm. We demonstrate these results using model simulations.

Related papers

• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Algorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences [7.552217586057245]
  We propose a simulation framework that mimics user-recommender system interactions in a long-term scenario. We introduce two novel metrics for quantifying the algorithm's impact on user preferences, specifically in terms of drift over time.
  arXiv  Detail & Related papers  (2024-09-24T21:54:22Z)
• Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
  We test for user strategization by conducting a lab experiment and survey. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes" Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
  arXiv  Detail & Related papers  (2024-05-09T07:36:08Z)
• AdaRec: Adaptive Sequential Recommendation for Reinforcing Long-term User Engagement [25.18963930580529]
  We introduce a novel paradigm called Adaptive Sequential Recommendation (AdaRec) to address this issue. AdaRec proposes a new distance-based representation loss to extract latent information from users' interaction trajectories. We conduct extensive empirical analyses in both simulator-based and live sequential recommendation tasks.
  arXiv  Detail & Related papers  (2023-10-06T02:45:21Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• Emergent Instabilities in Algorithmic Feedback Loops [3.4711828357576855]
  We explore algorithmic confounding in recommendation algorithms through teacher-student learning simulations. Results highlight the need to account for emergent behaviors from interactions between people and algorithms.
  arXiv  Detail & Related papers  (2022-01-18T18:58:03Z)
• Robust Value Iteration for Continuous Control Tasks [99.00362538261972]
  When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. We present Robust Fitted Value Iteration, which uses dynamic programming to compute the optimal value function on the compact state domain. We show that robust value is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm.
  arXiv  Detail & Related papers  (2021-05-25T19:48:35Z)
• Reinforcement Learning Beyond Expectation [11.428014000851535]
  Cumulative prospect theory (CPT) is a paradigm that has been empirically shown to model a tendency of humans to view gains and losses differently. In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment. In order to endow the agent with the ability to closely mimic the behavior of human users, we optimize a CPT-based cost.
  arXiv  Detail & Related papers  (2021-03-29T20:35:25Z)
• Learning User Preferences in Non-Stationary Environments [42.785926822853746]
  We introduce a novel model for online non-stationary recommendation systems. We show that our algorithm outperforms other static algorithms even when preferences do not change over time.
  arXiv  Detail & Related papers  (2021-01-29T10:26:16Z)
• Do Offline Metrics Predict Online Performance in Recommender Systems? [79.48653445643865]
  We investigate the extent to which offline metrics predict online performance by evaluating recommenders across six simulated environments. We observe that offline metrics are correlated with online performance over a range of environments. We study the impact of adding exploration strategies, and observe that their effectiveness, when compared to greedy recommendation, is highly dependent on the recommendation algorithm.
  arXiv  Detail & Related papers  (2020-11-07T01:41:13Z)

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.