Incentivising Exploration and Recommendations for Contextual Bandits with Payments

• URL: http://arxiv.org/abs/2001.07853v1
• Date: Wed, 22 Jan 2020 02:26:22 GMT
• Title: Incentivising Exploration and Recommendations for Contextual Bandits with Payments
• Authors: Priyank Agrawal and Theja Tulabandhula
• Abstract summary: We show how the platform can learn the inherent attributes of items and achieve a sublinear regret while maximizing cumulative social welfare. Our approach can improve various engagement metrics of users on e-commerce stores, recommendation engines and matching platforms.
• Score: 2.5966580648312223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize these agents to explore different items/recommendations, we show how the platform can learn the inherent attributes of items and achieve a sublinear regret while maximizing cumulative social welfare. We also calculate theoretical bounds on the cumulative costs of incentivization to the platform. Unlike previous works in this domain, we consider contexts to be completely adversarial, and the behavior of the adversary is unknown to the platform. Our approach can improve various engagement metrics of users on e-commerce stores, recommendation engines and matching platforms.

Related papers

• User Welfare Optimization in Recommender Systems with Competing Content Creators [65.25721571688369]
  In this study, we perform system-side user welfare optimization under a competitive game setting among content creators. We propose an algorithmic solution for the platform, which dynamically computes a sequence of weights for each user based on their satisfaction of the recommended content. These weights are then utilized to design mechanisms that adjust the recommendation policy or the post-recommendation rewards, thereby influencing creators' content production strategies.
  arXiv  Detail & Related papers  (2024-04-28T21:09:52Z)
• Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms [12.368291979686122]
  We propose a model for learning with bandit feedback while accounting for deterministically evolving and unobservable states. The workhorse applications of our model are learning for recommendation systems and learning for online ads.
  arXiv  Detail & Related papers  (2023-07-21T15:43:32Z)
• Online Learning in a Creator Economy [91.55437924091844]
  We study the creator economy as a three-party game between the users, platform, and content creators. We analyze two families of contracts: return-based contracts and feature-based contracts. We show that under smoothness assumptions, the joint optimization of return-based contracts and recommendation policy provides a regret.
  arXiv  Detail & Related papers  (2023-05-19T01:58:13Z)
• Interactive Learning with Pricing for Optimal and Stable Allocations in Markets [12.580391999838128]
  Large-scale online recommendation systems must facilitate the allocation of a limited number of items among competing users while learning their preferences from user feedback. Our framework enhances the quality of recommendations by exploring allocations that optimistically maximize the rewards. To minimize instability, a measure of users' incentives to deviate from recommended allocations, the algorithm prices the items based on a scheme derived from the Walrasian equilibria. Our approach is the first to integrate techniques from bandits, optimal resource allocation, and collaborative filtering to obtain an algorithm that achieves sub-linear social welfare regret as well as sub-linear instability.
  arXiv  Detail & Related papers  (2022-12-13T20:33:54Z)
• 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)
• Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management [61.88858330222619]
  We present an approach for predicting trust links between peers in social media. We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis. Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
  arXiv  Detail & Related papers  (2021-11-11T19:40:51Z)
• Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling [6.312395952874578]
  We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts.
  arXiv  Detail & Related papers  (2021-04-30T15:16:35Z)
• Incentivizing Exploration in Linear Bandits under Information Gap [50.220743323750035]
  We study the problem of incentivizing exploration for myopic users in linear bandits. In order to maximize the long-term reward, the system offers compensation to incentivize the users to pull the exploratory arms.
  arXiv  Detail & Related papers  (2021-04-08T16:01:56Z)
• Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning [106.51930957941433]
  In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous. This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning.
  arXiv  Detail & Related papers  (2020-05-31T12:45:06Z)

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.