检索结果-内蒙古大学图书馆

personalized recommendation system Based on Meta-Learning for Human-Centric Consumer Services in Gig Economy

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1080-1091页

作者： Ma, Yixuan Wang, Zhichao Tang, Mincong Qu, Wei Deveci, Muhammet Pamucar, Dragan Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Int Ctr Informat Res Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Natl Def Univ Dept Ind Engn Turkish Naval Acad TR-34940 Istanbul Turkiye Lebanese Amer Univ Dept Elect & Comp Engn Byblos 961 Lebanon Univ Belgrade Fac Org Sci Dept Operat Res & Stat Belgrade 11000 Serbia Yuan Ze Univ Coll Engn Taoyuan 320315 Taiwan

The gig economy has facilitated the growth of customized services through digital platforms that connect consumers with service providers. However, the surge in service providers has led to a "cold-start problem", which limits the effectiveness of personalized task recommendation systems. To address this challenge, this paper purposed a personalized recommendation system for human-centric consumer services in the gig economy. It addresses the problem by using meta-learning to generate suitable preference embeddings for workers with limited bidding history, interests, and working competence. The system includes a competence module with self-attention and interest modules to capture workers' personalized preferences. The model is evaluated on real-world datasets from ***, and the results demonstrate that it outperforms state-of-the-art models in accurately recommending suitable personalized tasks to both new and existing workers with skill-evolving. The proposed system can reduce task completion times and improve task quality by ensuring that tasks are assigned to the most suitable workers.

关键词： personalized recommendation system human-centric consumer services artificial intelligence meta-learning

来源：评论

学校读者我要写书评

暂无评论

personalized recommendation system of Resource Database for College Students' Innovation and Entrepreneurship 13

Personalized Recommendation System of Resource Database for ...

引用

13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Liu, Fang Sichuan Univ Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9781665438926

In order to improve the efficiency of College Students' innovation and entrepreneurship data recommendation, this paper designs a novel personalized recommendation system of resource database for college students' innovation and entrepreneurship. Relying on MVC mode and J2EE technology, this paper constructs the overall scheme and detailed design of an application information management system of College Students' innovation and entrepreneurship project resource database. The results show that the system can facilitate college students to submit materials related to innovation and entrepreneurship projects, and improve the efficiency of information audit and management.

关键词： J2EE personalized recommendation system Project management Innovation and entrepreneurship

来源：评论

学校读者我要写书评

暂无评论

A personalized recommendation system based on product taxonomy for one-to-one marketing online

引用

EXPERT systemS WITH APPLICATIONS 2005年第2期29卷 383-392页

作者： Hung, LP No Taiwan Inst Sci & Technol Dept Informat Management Taipei 112 Taiwan

Nowadays, Electronic Commerce (EC) provides a new gateway for customers shopping online. One of the most significant advantages offered by online shops is convenience. Online shopping is no longer a time-consuming task and, in fact, is an energy-saving activity. Therefore, shortening customers' product searching time is the key to an online shop's success. In order to serve customers instantly and efficiently, it is essential to recognize each customer's unique and particular needs and recommend a personalized shopping list. In this paper, we construct a recommendation system based on a modified product taxonomy and customer classification to identify customers' shopping behavior: product addictive, brand addictive or a hybrid addictive. By analyzing each customer's preferred brand or product, our proposed system can recommend products to customers either at the general or at the specific levels. (C) 2005 Elsevier Ltd. All rights reserved.

关键词： personalized recommendation system one-to-one marketing product taxonomy customer classification

来源：评论

学校读者我要写书评

暂无评论

Media personalized recommendation system Based on Network Algorithm 6

Media Personalized Recommendation System Based on Network Al...

引用

6th IEEE Advanced Information Technology, Electronic and Automation Control Conference (IEEE IAEAC)

作者： Jiang, Wendan Yangtze Univ Sch Arts & Media Wuhan 430199 Hubei Peoples R China

ISBN: (数字)9781665458641

ISBN: (纸本)9781665458641

With information overload, a new feature of the Internet, a large amount of irrelevant, useless and redundant information on Weibo seriously interferes with people's accurate selection of required information. Users need to accurately and quickly obtain the marketing content they need through the media personalized recommendation system. This paper designs a media personalized recommendation system based on network algorithm, discusses the functional requirements of media personalized recommendation and the classification of personalized recommendation system based on network algorithm. Through the experimental test of the media personalized recommendation system, taking Weibo as an example, the data ratio between the number of effective recommendations and several users in each category is tested. The test results show that the correspondence rate between the information recommendation results and user interests of the personalized recommendation system studied in this paper is 79.7%, which indicates that the interest analysis of the user model of the media personalized recommendation system designed in this paper is more accurate. The validity of the algorithm is verified.

关键词： Network Algorithm Media personalized recommendation system system Design

来源：评论

学校读者我要写书评

暂无评论

Research on Student Accurate Portrait personalized recommendation system Based on Improved KMeans Algorithm 24

Research on Student Accurate Portrait Personalized Recommend...

引用

International Conference on Algorithms, Software Engineering, and Network Security (ASENS)

作者： Zhang, Jiameng Shandong Inst Commerce & Technol Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9798400709784

In an era dominated by big data, this study focuses on creating precise student profiles and developing personalized recommendation systems, leveraging advancements in big data technologies and algorithms. It delves into the significance of student profiling and personalized recommendations, highlighting the constraints of traditional KMeans algorithms in this context. The research thoroughly explores big data analysis concepts, detailing the KMeans algorithm's principles, limitations, and the theoretical basis for its enhancement. Central to the study is the role of big data in building student profiles, encompassing data collection, cleansing, and feature extraction processes. Utilizing an improved KMeans algorithm, the study achieves enhanced accuracy and detail in student profiling. Additionally, it designs a personalized recommendation system by selecting suitable algorithms that align with student profiles and proposes metrics to evaluate their effectiveness. The experiments conducted affirm the proposed methods and system's efficacy. This research contributes to the field by applying big data and algorithmic improvements to refine student profiles and recommendation systems, while acknowledging existing challenges in data processing and algorithm scalability, and suggesting directions for future optimization.

关键词： Big data analysis Student accurate portraits personalized recommendation system KMeans algorithm Algorithm improvement

来源：评论

学校读者我要写书评

暂无评论

Information Acquisition and Analysis Technology of personalized recommendation system Based on Case-Based Reasoning for Internet of Things 10

Information Acquisition and Analysis Technology of Personali...

引用

10th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Sun, Jieli Zhai, Yao Zhao, Yanxia Li, Jianke Yan, Naishi Hebei Univ Econ & Business Coll Informat Technol Shijiazhuang Hebei Peoples R China George Washington Univ Sch Engn & Appl Sci Washington DC 20052 USA Beijing Univ Posts & Telecommun Sch Software Engn Beijing Peoples R China

ISBN: (数字)9781728109749

ISBN: (纸本)9781728109749

In the paper, we discuss the theories of the information acquisition and analysis and the information quality of the case-based reasoning (CBR) personalized recommendation system. We also take a deep study of the key techniques of acquiring and analyzing information quality. With research results of this paper, combined with the content based recommendation technology and recommendation results of collaborative filtering, a CBR-based personalized combinatorial recommendation algorithm is designed.

关键词： personalized recommendation system information quality case-based reasoning (CBR) recommendation algorithm collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

A personalized recommendation system Combining Case-Based Reasoning and User-Based Collaborative Filtering

A Personalized Recommendation System Combining Case-Based Re...

引用

21st Chinese Control and Decision Conference

作者： Zhu, XiaoMing Ye, HongWu Gong, SongJie Zhejiang Business Technol Inst Ningbo 315012 Zhejiang Peoples R China Zhejiang Text & Fash Coll Ningbo 315211 Zhejiang Peoples R China

ISBN: (纸本)9781424427239

personalized recommendation systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is one of the most important technologies in recommender systems. Such technology recommends items to a customer according to the preference data of similar customers. The main problems of collaborative filtering are about prediction accuracy and data sparsity. To solve these problems, this paper presents a personalized recommendation algorithm combining case-based reasoning and user-based collaborative filtering. Firstly, it employs case-based reasoning technology to fill the vacant ratings of the user-item matrix. Then, it produces prediction of the target user to the target item using user-based collaborative filtering. The personalized recommendation system combining case-based reasoning and user-based collaborative filtering can alleviate the sparsity issue and can produce more accuracy recommendation than the traditional recommender systems.

关键词： personalized recommendation system Collaborative Filtering Sparsity Case-based Reasoning

来源：评论

学校读者我要写书评

暂无评论

The Study of User Model of personalized recommendation system based on Linked Course Data

The Study of User Model of Personalized Recommendation Syste...

引用

International Forum on Computer and Information Technology (IFCIT)

作者： Wu Jiyan Wu Chanle Wuhan Univ Comp Sch Wuhan Peoples R China Natl Engn Res Ctr Multimedia Software Wuhan Peoples R China Wuhan Univ Comp Sch Wuhan Peoples R China

ISBN: (纸本)9783038350194

The massive information on Internet makes users obtain information become in efficient. Similarly, the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable, etc. Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental, the application shows that the model can indicate the user's learning requirements, and rational and efficient learning path is recommended.

关键词： Semantic Web Linked Open Data Linked Course Data personalized recommendation system E-learning

来源：评论

学校读者我要写书评

暂无评论

Building personalized recommendation system in e-commerce using association rule-based mining and classification

Building personalized recommendation system in e-commerce us...

引用

6th International Conference on Machine Learning and Cybernetics

作者： Zhang, Xi-Zheng Hunan Inst Engn Dept Comp Sci Xiangtan 411104 Peoples R China

ISBN: (纸本)9781424409723

Due to the convenience of Internet, people can search for whatever information they need and buy whatever they want on the web. In the age of E-Commerce, it is difficult to provide support for customers to find the most valuable products that match their heterogeneous needs. Traditional approaches to this so-called personalization problem adopt predefined formats to describe the customer requirements. This always leads to distortion in eliciting requirement information and thus inaccurate recommendations. In this paper, we propose a personalized recommendation system using association rule mining and classification in e-commerce. Customer requirements are extracted from text documents and transformed into a set of significant phrases. Allowing the transformed transaction records, a set of association rule are mined from database using Apriori algorithm. CBA-CB algorithm is applied to produce the best rules out of the whole set of rules. The best classifiers are then generated after the test and validation of those rules, aimed to predict the item labels for new customer requirements and thus assigns the corresponding class labels to the customer. The system analysis and design of the proposed recommendation system as well as the implementation of prototype are also presented.

关键词： personalized recommendation system association rule mining classification E-Commerce

来源：评论

学校读者我要写书评

暂无评论

Convergence Improvement of Q-learning Based on a personalized recommendation system

Convergence Improvement of Q-learning Based on a Personalize...

引用

International Automatic Control Conference (CACS)

作者： Chiang, Chia-Ling Cheng, Ming-Yang Ye, Ting-Yu Chen, Ya-Ling Huang, Pin-Hsuan Natl Cheng Kung Univ Dept Elect Engn Tainan 701 Taiwan

ISBN: (纸本)9781728138466

Reinforcement Learning (RL) - a research branch of artificial intelligence - exploits the concept of reward/penalty from human learning so that the feedback information of the environment can be used for self-learning without previous knowledge. Although RL has been applied to many research fields, there are difficulties when implementing it in some real world applications. One difficulty is the tradeoff between exploration and exploitation for the agent of RL in choosing a proper action. An improper action may lead to a learning failure or an increase in learning cost. Another difficulty is that the learning agent of RL needs to interact with the environment to attain a real-time reward/penalty;however, the learning time spent in the interaction process may be too long. In order to overcome the aforementioned difficulties, this paper proposes an approach that employs a personalized recommendation system to provide a feedforward candidate action for RL to implement self-adaptive learning through teaching. A real visual tracking experiment using a pan-tilt camera system is conducted to assess the performance of the proposed approach. Experimental results show that the proposed personalized recommendation system-based approach is able to improve the effectiveness and practicality of RL.

关键词： Reinforcement Learning Q-learning personalized recommendation system Visual Tracking

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：