The Nah Bandit: Modeling User Non-compliance in Recommendation Systems

• URL: http://arxiv.org/abs/2408.07897v1
• Date: Thu, 15 Aug 2024 03:01:02 GMT
• Title: The Nah Bandit: Modeling User Non-compliance in Recommendation Systems
• Authors: Tianyue Zhou, Jung-Hoon Cho, Cathy Wu,
• Abstract summary: Expert with Clustering (EWC) is a hierarchical approach that incorporates feedback from both recommended and non-recommended options to accelerate user preference learning. EWC outperforms both supervised learning and traditional contextual bandit approaches. This work lays the foundation for future research in Nah Bandit, providing a robust framework for more effective recommendation systems.
• Score: 2.421459418045937
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommendation systems now pervade the digital world, ranging from advertising to entertainment. However, it remains challenging to implement effective recommendation systems in the physical world, such as in mobility or health. This work focuses on a key challenge: in the physical world, it is often easy for the user to opt out of taking any recommendation if they are not to her liking, and to fall back to her baseline behavior. It is thus crucial in cyber-physical recommendation systems to operate with an interaction model that is aware of such user behavior, lest the user abandon the recommendations altogether. This paper thus introduces the Nah Bandit, a tongue-in-cheek reference to describe a Bandit problem where users can say `nah' to the recommendation and opt for their preferred option instead. As such, this problem lies in between a typical bandit setup and supervised learning. We model the user non-compliance by parameterizing an anchoring effect of recommendations on users. We then propose the Expert with Clustering (EWC) algorithm, a hierarchical approach that incorporates feedback from both recommended and non-recommended options to accelerate user preference learning. In a recommendation scenario with $N$ users, $T$ rounds per user, and $K$ clusters, EWC achieves a regret bound of $O(N\sqrt{T\log K} + NT)$, achieving superior theoretical performance in the short term compared to LinUCB algorithm. Experimental results also highlight that EWC outperforms both supervised learning and traditional contextual bandit approaches. This advancement reveals that effective use of non-compliance feedback can accelerate preference learning and improve recommendation accuracy. This work lays the foundation for future research in Nah Bandit, providing a robust framework for more effective recommendation systems.

Related papers

• Treatment Effect Estimation for User Interest Exploration on Recommender Systems [10.05609996672672]
  We propose an Uplift model-based Recommender framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards.
  arXiv  Detail & Related papers  (2024-05-14T13:22:33Z)
• Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought [43.490918008927]
  We propose shadow-free MIAs that directly leverage a user's recommendations for membership inference. Our attack achieves far better attack accuracy with low false positive rates than baselines.
  arXiv  Detail & Related papers  (2024-05-11T13:52:22Z)
• Expert with Clustering: Hierarchical Online Preference Learning Framework [4.05836962263239]
  Expert with Clustering (EWC) is a hierarchical contextual bandit framework that integrates clustering techniques and prediction with expert advice. EWC can substantially reduce regret by 27.57% compared to the LinUCB baseline.
  arXiv  Detail & Related papers  (2024-01-26T18:44:49Z)
• Incentive-Aware Recommender Systems in Two-Sided Markets [49.692453629365204]
  We propose a novel recommender system that aligns with agents' incentives while achieving myopically optimal performance. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets. Both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation.
  arXiv  Detail & Related papers  (2022-11-23T22:20:12Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Modeling Attrition in Recommender Systems with Departing Bandits [84.85560764274399]
  We propose a novel multi-armed bandit setup that captures policy-dependent horizons. We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal. We then move forward to the more challenging case, where users are divided among two types.
  arXiv  Detail & Related papers  (2022-03-25T02:30:54Z)
• Learning the Optimal Recommendation from Explorative Users [38.332330484187395]
  We study the sequential interactions between a recommender system and a user. We show that efficient system learning is still possible but is more difficult.
  arXiv  Detail & Related papers  (2021-10-06T21:01:18Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Fairness-Aware Explainable Recommendation over Knowledge Graphs [73.81994676695346]
  We analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations, due to insufficient training data for the inactive users. We propose a fairness constrained approach via re-ranking to mitigate this problem in the context of explainable recommendation over knowledge graphs.
  arXiv  Detail & Related papers  (2020-06-03T05:04:38Z)
• Reward Constrained Interactive Recommendation with Natural Language Feedback [158.8095688415973]
  We propose a novel constraint-augmented reinforcement learning (RL) framework to efficiently incorporate user preferences over time. Specifically, we leverage a discriminator to detect recommendations violating user historical preference. Our proposed framework is general and is further extended to the task of constrained text generation.
  arXiv  Detail & Related papers  (2020-05-04T16:23:34Z)

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.