Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning

• URL: http://arxiv.org/abs/2210.10638v1
• Date: Thu, 6 Oct 2022 16:01:26 GMT
• Title: Digital Human Interactive Recommendation Decision-Making Based on Reinforcement Learning
• Authors: Junwu Xiong (AntGroup)
• Abstract summary: We design a novel digital human interactive recommendation agent framework based on reinforcement learning. The proposed framework learns through immediate interactions among the digital human and customers. Experiments on real business data show that this framework can provide better-personalized customer engagement and better customer experiences.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital human recommendation system has been developed to help customers to find their favorite products and is playing an active role in various recommendation contexts. How to catch and learn the preferences of the customers at the right time and meet the exact requirements of the customer become crucial in the digital human recommendation. We design a novel practical digital human interactive recommendation agent framework based on reinforcement learning to improve the efficiency of interactive recommendation decision-making by leveraging both the digital human features and the superiority of reinforcement learning. The proposed framework learns through immediate interactions among the digital human and customers dynamically through stat-of-art reinforcement learning algorithms and embedding with multimodal and graph embedding to improve the accuracy of the personalization and thus enable the digital human agent to actively catch the attention of a customer timely. Experiments on real business data show that this framework can provide better-personalized customer engagement and better customer experiences etc.

Related papers

• Communication Learning in Multi-Agent Systems from Graph Modeling Perspective [62.13508281188895]
  We introduce a novel approach wherein we conceptualize the communication architecture among agents as a learnable graph. We introduce a temporal gating mechanism for each agent, enabling dynamic decisions on whether to receive shared information at a given time.
  arXiv  Detail & Related papers  (2024-11-01T05:56:51Z)
• MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning [99.09906827676748]
  We introduce MotionRL, the first approach to utilize Multi-Reward Reinforcement Learning (RL) for optimizing text-to-motion generation tasks. Our novel approach uses reinforcement learning to fine-tune the motion generator based on human preferences prior knowledge of the human perception model. In addition, MotionRL introduces a novel multi-objective optimization strategy to approximate optimality between text adherence, motion quality, and human preferences.
  arXiv  Detail & Related papers  (2024-10-09T03:27:14Z)
• Digital assistant in a point of sales [0.0]
  This article investigates the deployment of a Voice User Interface (VUI)-powered digital assistant in a retail setting. By integrating a digital assistant into a high-traffic retail environment, we evaluate its effectiveness in improving the quality of customer service.
  arXiv  Detail & Related papers  (2024-06-07T11:33:21Z)
• Enhanced User Interaction in Operating Systems through Machine Learning Language Models [17.09116903102371]
  This paper explores the potential applications of large language models, machine learning and interaction design for user interaction in recommendation systems and operating systems. The combination of interaction design and machine learning can provide a more efficient and personalized user experience for products and services.
  arXiv  Detail & Related papers  (2024-02-24T12:17:06Z)
• From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations [0.0]
  This research aims to explore the impact of Machine Learning (ML) on the evolution and efficacy of Recommendation Systems (RS) The research identifies the increasing expectation of users for a seamless, intuitive online experience, where content is personalized and dynamically adapted to changing preferences.
  arXiv  Detail & Related papers  (2024-02-12T22:56:18Z)
• Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
  We propose a method for a collaborative system that remains safe by having a human making decisions. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
  arXiv  Detail & Related papers  (2023-11-13T16:00:16Z)
• RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents [30.250555783628762]
  This research argues that addressing these issues is not solely the recommender systems' responsibility. We introduce the RAH Recommender system, Assistant, and Human framework, emphasizing the alignment with user personalities. Our contributions provide a human-centered recommendation framework that partners effectively with various recommendation models.
  arXiv  Detail & Related papers  (2023-08-19T04:46:01Z)
• Assisting Human Decisions in Document Matching [52.79491990823573]
  We devise a proxy matching task that allows us to evaluate which kinds of assistive information improve decision makers' performance. We find that providing black-box model explanations reduces users' accuracy on the matching task. On the other hand, custom methods that are designed to closely attend to some task-specific desiderata are found to be effective in improving user performance.
  arXiv  Detail & Related papers  (2023-02-16T17:45:20Z)
• Generative Inverse Deep Reinforcement Learning for Online Recommendation [62.09946317831129]
  We propose a novel inverse reinforcement learning approach, namely InvRec, for online recommendation. InvRec extracts the reward function from user's behaviors automatically, for online recommendation.
  arXiv  Detail & Related papers  (2020-11-04T12:12:25Z)
• Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning [2.5799044614524664]
  This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Results show users giving informative advice provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode.
  arXiv  Detail & Related papers  (2020-09-21T02:14:02Z)
• Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation [49.32287384774351]
  Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. We propose Knowledge-Guided deep Reinforcement learning to harness the advantages of both reinforcement learning and knowledge graphs for interactive recommendation.
  arXiv  Detail & Related papers  (2020-04-17T05:26:47Z)

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.