Deviation-Based Learning

• URL: http://arxiv.org/abs/2109.09816v1
• Date: Mon, 20 Sep 2021 19:51:37 GMT
• Title: Deviation-Based Learning
• Authors: Junpei Komiyama and Shunya Noda
• Abstract summary: We propose deviation-based learning, a new approach to training recommender systems. We show that learning frequently stalls if the recommender always recommends a choice. Social welfare and the learning rate are improved drastically if the recommender abstains from recommending a choice.
• Score: 5.304857921982131
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose deviation-based learning, a new approach to training recommender systems. In the beginning, the recommender and rational users have different pieces of knowledge, and the recommender needs to learn the users' knowledge to make better recommendations. The recommender learns users' knowledge by observing whether each user followed or deviated from her recommendations. We show that learning frequently stalls if the recommender always recommends a choice: users tend to follow the recommendation blindly, and their choices do not reflect their knowledge. Social welfare and the learning rate are improved drastically if the recommender abstains from recommending a choice when she predicts that multiple arms will produce a similar payoff.

Related papers

• Interactive Garment Recommendation with User in the Loop [77.35411131350833]
  We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outfit. We present a reinforcement learning agent capable of suggesting appropriate garments and ingesting user feedback to improve its recommendations.
  arXiv  Detail & Related papers  (2024-02-18T16:01:28Z)
• Solutions to preference manipulation in recommender systems require knowledge of meta-preferences [7.310043452300736]
  Some preference changes on the part of the user are self-induced and desired whether the recommender caused them or not. This paper proposes that solutions to preference manipulation in recommender systems must take into account certain meta-preferences.
  arXiv  Detail & Related papers  (2022-09-14T15:01:13Z)
• Explicit User Manipulation in Reinforcement Learning Based Recommender Systems [0.0]
  Reinforcement learning based recommender systems can learn to influence users if that means maximizing clicks, engagement, or consumption. Social media has been shown to be a contributing factor to increased political polarization. explicit user manipulation in which the beliefs and opinions of users are tailored towards a certain end emerges as a significant concern.
  arXiv  Detail & Related papers  (2022-03-20T19:03:18Z)
• KnowledgeCheckR: Intelligent Techniques for Counteracting Forgetting [52.623349754076024]
  We provide an overview of the recommendation approaches integrated in KnowledgeCheckR. Examples thereof are utility-based recommendation that helps to identify learning contents to be repeated in the future, collaborative filtering approaches that help to implement session-based recommendation, and content-based recommendation that supports intelligent question answering.
  arXiv  Detail & Related papers  (2021-02-15T20:06:28Z)
• ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models [26.11434743591804]
  We devise a human-in-the-loop framework, called ELIXIR, where user feedback on explanations is leveraged for pairwise learning of user preferences. ELIXIR leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart.
  arXiv  Detail & Related papers  (2021-02-15T13:43:49Z)
• Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect [89.91745908462417]
  We show the benefits of pre-training to recommender systems through experiments. We discuss several promising directions for future research for recommender systems with pre-training.
  arXiv  Detail & Related papers  (2020-09-19T13:06:27Z)
• DeepFair: Deep Learning for Improving Fairness in Recommender Systems [63.732639864601914]
  The lack of bias management in Recommender Systems leads to minority groups receiving unfair recommendations. We propose a Deep Learning based Collaborative Filtering algorithm that provides recommendations with an optimum balance between fairness and accuracy without knowing demographic information about the users.
  arXiv  Detail & Related papers  (2020-06-09T13:39:38Z)
• 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)
• Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [48.2733163413522]
  It becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems.
  arXiv  Detail & Related papers  (2020-04-08T02:11:55Z)

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.