Enhanced User Interaction in Operating Systems through Machine Learning Language Models

• URL: http://arxiv.org/abs/2403.00806v1
• Date: Sat, 24 Feb 2024 12:17:06 GMT
• Title: Enhanced User Interaction in Operating Systems through Machine Learning Language Models
• Authors: Chenwei Zhang, Wenran Lu, Chunhe Ni, Hongbo Wang, Jiang Wu
• Abstract summary: 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.
• Score: 17.09116903102371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the large language model showing human-like logical reasoning and understanding ability, whether agents based on the large language model can simulate the interaction behavior of real users, so as to build a reliable virtual recommendation A/B test scene to help the application of recommendation research is an urgent, important and economic value problem. The combination of interaction design and machine learning can provide a more efficient and personalized user experience for products and services. This personalized service can meet the specific needs of users and improve user satisfaction and loyalty. Second, the interactive system can understand the user's views and needs for the product by providing a good user interface and interactive experience, and then use machine learning algorithms to improve and optimize the product. This iterative optimization process can continuously improve the quality and performance of the product to meet the changing needs of users. At the same time, designers need to consider how these algorithms and tools can be combined with interactive systems to provide a good user experience. This paper explores the potential applications of large language models, machine learning and interaction design for user interaction in recommendation systems and operating systems. By integrating these technologies, more intelligent and personalized services can be provided to meet user needs and promote continuous improvement and optimization of products. This is of great value for both recommendation research and user experience applications.

Related papers

• Investigating the effect of Mental Models in User Interaction with an Adaptive Dialog Agent [27.75972750138208]
  Mental models play an important role in whether user interaction with intelligent systems is successful or not. We show that users have a variety of conflicting mental models about such systems. We show that adapting a dialog agent's behavior to better align with users' mental models, even when done implicitly, can improve perceived usability, dialog efficiency, and success.
  arXiv  Detail & Related papers  (2024-08-26T09:57:19Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach [55.762798168494726]
  We present a novel Large Language Model (LLM)-based approach for validating the implementation of functional NL-based requirements in a graphical user interface (GUI) prototype. Our approach aims to detect functional user stories that are not implemented in a GUI prototype and provides recommendations for suitable GUI components directly implementing the requirements.
  arXiv  Detail & Related papers  (2024-06-12T11:59:26Z)
• Reinforcement Learning-Based Framework for the Intelligent Adaptation of User Interfaces [0.0]
  Adapting the user interface (UI) of software systems to meet the needs and preferences of users is a complex task. Recent advances in Machine Learning (ML) techniques may provide effective means to support the adaptation process. In this paper, we instantiate a reference framework for Intelligent User Interface Adaptation by using Reinforcement Learning (RL) as the ML component.
  arXiv  Detail & Related papers  (2024-05-15T11:14:33Z)
• Unlocking Adaptive User Experience with Generative AI [8.578448990789965]
  We develop user personas and adaptive interfaces using both ChatGPT and a traditional manual process. To obtain data for the personas we collected data from 37 survey participants and 4 interviews. The comparison of ChatGPT generated content and manual content indicates promising results.
  arXiv  Detail & Related papers  (2024-04-08T12:22:39Z)
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
  We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
  arXiv  Detail & Related papers  (2023-10-13T16:37:14Z)
• Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models [10.892991111926573]
  We present a paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system. Within this innovative framework, user requests issued to the machine are handled by an interconnected ecosystem of generative AI models. This visionary concept raises significant challenges, including privacy, security, trustability, and the ethical use of generative models.
  arXiv  Detail & Related papers  (2023-10-07T17:16:34Z)
• Interactive Text Generation [75.23894005664533]
  We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts.
  arXiv  Detail & Related papers  (2023-03-02T01:57:17Z)
• Optimizing Interactive Systems via Data-Driven Objectives [70.3578528542663]
  We propose an approach that infers the objective directly from observed user interactions. These inferences can be made regardless of prior knowledge and across different types of user behavior. We introduce Interactive System (ISO), a novel algorithm that uses these inferred objectives for optimization.
  arXiv  Detail & Related papers  (2020-06-19T20:49:14Z)
• Empowering Active Learning to Jointly Optimize System and User Demands [70.66168547821019]
  We propose a new active learning approach that jointly optimize the active learning system (training efficiently) and the user (receiving useful instances) We study our approach in an educational application, which particularly benefits from this technique as the system needs to rapidly learn to predict the appropriateness of an exercise to a particular user. We evaluate multiple learning strategies and user types with data from real users and find that our joint approach better satisfies both objectives when alternative methods lead to many unsuitable exercises for end users.
  arXiv  Detail & Related papers  (2020-05-09T16:02:52Z)

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.