检索结果-内蒙古大学图书馆

Music recommendation Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function

APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2022年第1期8卷 1769-1776页

作者： Wu, Jianfeng Henan Univ Engn Zhixing Coll Zhengzhou 451191 Henan Peoples R China

This paper proposes a simulation algorithm of transition probability function based on logistic distribution. This method mainly models popularity and state transition probability functions by acquiring consumers' music preferences and likes. Through this mathematical model, this paper obtains the best results that are more in line with consumer preference. This paper conducts a simulation experiment by collecting Netease cloud music data. Finally, through the comparison with the empirical data, it is further demonstrated that the algorithm model in this paper has particular practical value.

关键词： recommendation algorithm Logistic distribution fitting Transition probability function Music recommendation index

来源：评论

学校读者我要写书评

暂无评论

Textile pattern recommendations with convolutional neural networks and autoencoder

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第18期35卷

作者： Mao, Kuang Wu, Sai He, Jiajia Huang, Haichao Yin, Yanlong Ren, Zujie Zhejiang Lab Res Ctr Next Generat Intelligent Comp Syst Hangzhou Peoples R China Key Lab Intelligent Comp Based Big Data Zhejiang Hangzhou Peoples R China Muri Technol Pte Ltd Hangzhou Peoples R China State Grid Zhejiang Elect Power Co Informat & Telecommun Branch Hangzhou Peoples R China

Textile pattern design is a time-consuming and tedious work. Mihui, our ongoing developing system, employs deep-learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation module of Mihui. The module forwards all generated pattern images to a deep encoding network, where images are mapped into 128-dimension vectors. For each user of Mihui, we create a profile by his/her purchased or downloaded history. A novel encoder network is proposed to learn a personal taste vector for each user, based on which, we recommend new patterns to him/her. Our records in Mihui show that the recommendation module effectively improve users' experience on Mihui.

关键词： autoencoder CNN recommendation algorithm textile pattern

来源：评论

学校读者我要写书评

暂无评论

SNS Based recommendation algorithm

SNS Based Recommendation Algorithm

引用

4th International Conference on Information Science and Applications (ICISA)

作者： Yoo, So-Yeop Jeong, Ok-Ran Gachon Univ Dept Software Design & Management Songnam South Korea

ISBN: (纸本)9781479906048;9781479906024

Social network service (SNS) based recommendation system is playing an important role in searching information in the vast number of SNS community websites. In this paper, we propose a recommendation algorithm using intimacy information based on the SNS after extracting information on the social network having relationship with the user. It is an algorithm to recommend the contents with high relationship based on the list of friends who are predicted as actually having close relationship with the user according to the familiarity after extracting the list of friends within the SNS using SNS information of the user.

关键词： SNS (social network service) recommendation algorithm intimacy information

来源：评论

学校读者我要写书评

暂无评论

Improved Collaborative Filtering recommendation algorithm based on Weighted Association Rules

Improved Collaborative Filtering Recommendation Algorithm ba...

引用

2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Yang, Hai Shandong Jiaotong Univ Coll Informat Sci & Elect Engn Jinan 250023 Shandong Peoples R China

ISBN: (纸本)9783037858646

In this paper the collaborative filtering recommendation and association rules are introduced firstly. Aiming at the shortcomings of basic collaborative filtering recommendation, this paper proposes an improved collaborative filtering recommendation algorithm based on weighted association rules (CFRA-WAR). Finally the simulation experiments are carried out to verify the validity of the improved recommendation algorithm. The results of simulation experiments show that the recommendation accuracy of CFRA-WAR is superior to basic collaborative filtering recommendation algorithm, although the algorithm time of CFRA-WAR is a little longer.

关键词： recommendation algorithm collaborative filtering weighted association rules calculation of similarity

来源：评论

学校读者我要写书评

暂无评论

A Matching recommendation algorithm for Celebrity Endorsement on Social Network

A Matching Recommendation Algorithm for Celebrity Endorsemen...

引用

20th International Annual Conference on Management Science and Engineering

作者： Lv Hai-xia Yu Guang Tian Xian-yun Harbin Inst Technol Sch Management Harbin 150001 Peoples R China

ISBN: (纸本)9781479904716

In this paper, a new matching recommendation algorithm is proposed to help an enterprise to find one or several proper celebrities as their product endorsement. The fans group of a celebrity, his impaction value in the social network and the matching degree between the celebrity and the product are used to determine the most suitable celebrity in social network. The attribute similarities between the target customers and the fans of the celebrity are calculated via the Pearson similarity formula. Then, considering the impaction value of the celebrity and the matching degree of the celebrity and the product which can be accessed on the website or usually available from the enterprise, an evaluation index is proposed. We use some data from Sina Micro-blog to show the effectiveness of our proposed matching recommendation algorithm. Moreover, the analysis shows that the products may be different which are endorsed by the same celebrity in and off the social network.

关键词： celebrity endorsement social networks micro-blog recommendation algorithm

来源：评论

学校读者我要写书评

暂无评论

Analysis and study of the process enterprise knowledge management model based on the recommendation algorithm

Analysis and study of the process enterprise knowledge manag...

引用

2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Yang, Zhenyu Zhou, Jun Qilu Univ Technol Jinan Jinan Peoples R China

ISBN: (纸本)9783037858646

The knowledge sharing is one of the most important characteristics of knowledge management. In the traditional model of knowledge management, employees only select the sharing knowledge through independent action, and operating behavior between employees of the same type did not reflect reference. This paper is the integration of the recommendation algorithm of data mining and the traditional knowledge ontology knowledge management model, proposing the process enterprise knowledge management model based on the recommendation algorithm, and knowledge management framework of knowledge as the main body, the field of process-driven and recommendation process for the behavior. To recommend the appropriate knowledge for the staff improves the efficiency of enterprise employees staff to the knowledge and promote the application and innovation of knowledge of the enterprise.

关键词： recommendation algorithm knowledge management model knowledge modeling

来源：评论

学校读者我要写书评

暂无评论

Deep recommendation Model Combining Long-and Short-Term Interest Preferences

引用

IEEE ACCESS 2021年 9卷 166455-166464页

作者： Niu, Lushuai Peng, Yan Liu, Yimao Sichuan Univ Sci & Engn Coll Automat & Informat Engn Yibin Peoples R China

The existing sequential recommendation algorithms cannot effectively capture and solve the problems such as the dynamic preferences of users over time. This paper proposes a deep recommendation model CLSR (Combines Long-term and Short-term interest recommendation) that Combines long-term and short-term interest preferences. Firstly, the model models the potential feature representation of users and items, and uses the self-attention mechanism to capture the relationship between items in the interaction of users' historical behavior, so as to better learn the short-term interest representation of users. At the same time, the BiGRU network is used to extract the features of users' long-term interests on a deep level. Finally, the features of long-term and short-term interest are fused. On four publicly available datasets, experimental results show that the proposed method has better improvement on HR@N, NDCG@N and MRR@N, which validates the effectiveness of the model.

关键词： Mathematical models Logic gates Data mining Data models Predictive models Licenses Context modeling recommendation algorithm self-attention mechanism bidirectional gating cyclic network sequence recommendation deep learning

来源：评论

学校读者我要写书评

暂无评论

Research on Personalized Learning Route Model Based on Improved Collaborative Filtering algorithm 2

Research on Personalized Learning Route Model Based on Impro...

引用

2nd International Conference on Big Data and Artificial Intelligence and Software Engineering (ICBASE)

作者： Wang, Hongbin Wei, Zhengang Ocean Univ China Dept Comp Applicat Technol Qingdao Shandong Peoples R China Ocean Univ China Dept Software Engn Qingdao Shandong Peoples R China

ISBN: (纸本)9781665427098

In order to solve the contradiction between the free course selection mode and blind course selection, this paper combines the knowledge-based recommendation algorithm model and the memory based collaborative filtering recommendation model, and proposes an improved collaborative filtering recommendation algorithm to mine the implicit learning order and association relationship between courses and provide recommendations for freshmen, This algorithm solves the cold start problem caused by the data sparsity problem of the traditional collaborative filtering recommendation algorithm, so that the algorithm can still give high-quality recommendation results when the initial data is extremely sparse. The experimental results show that compared with the traditional recommendation algorithm, the accuracy and recall of this method have been improved, good recommendation results are obtained on real data sets.

关键词： recommendation algorithm collaborative filtering intelligent teaching data mining

来源：评论

学校读者我要写书评

暂无评论

Application of Intelligent recommendation for Agricultural Information: A Systematic Literature Review

引用

IEEE ACCESS 2021年 9卷 153616-153632页

作者： Song, Caixia Dong, Haoyu Qingdao Agr Univ Coll Sci & Informat Qingdao 266109 Peoples R China

With the ever-growing volume of online information, recommender system has been an effective strategy to overcome such information overload. Recommender systems are widely used in many web applications, such as e-commerce, news, agriculture and other fields. Agricultural informatization is an important driving force for the development of agricultural modernization. With the further improvement of agricultural informatization infrastructure construction, the use of modern information technology to achieve personalized agricultural information resource recommendation services and provide users with timely and effective information has become an effective solution. This article aims to provide a comprehensive review of recent research efforts on application of agricultural information based on intelligent recommender systems. Firstly, the method of content analysis used in this article to sort out the papers is introduced. Secondly, the background concepts of recommender systems and the key technologies are presented. Thirdly, the applications of recommender systems/technologies for agricultural information are described in detail. Finally, a summary and outlook on the application of recommender systems for agricultural information are provided.

关键词： Recommender systems Collaboration Social networking (online) Clustering algorithms Agricultural products Prediction algorithms Licenses recommendation algorithm recommender system application model agricultural information collaborative filtering hybrid recommendation

来源：评论

学校读者我要写书评

暂无评论

Click-Through Rate Prediction algorithm Based on Modeling of Implicit High-Order Feature Importance

引用

JOURNAL OF INTERNET TECHNOLOGY 2022年第5期23卷 1077-1086页

作者： Yang, Qing Li, Ning Hu, Shiyan Li, Heyong Zhang, Jingwei Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instru Guilin Guangxi Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Guangxi Peoples R China

Click-through rate (CTR) prediction plays a central role in online advertising and recommendation systems. In recent years, with the successful application of deep neural networks (DNNs) in many fields, researchers have integrated deep learning into CTR prediction algorithms to model implicit high-order features. However, most of these existing methods unify the weights of implicit higher-order features to predict user behaviors. The importance of such features of different dimensions for predicting user click behaviors are different. Base on this, we propose a prediction method that dynamically learns the importance of implicit high-order features. Specifically, we integrate the output features of deep and shallow components, and adaptively learn the weights of implicit high-order features from among all features through the designed attention network, which effectively capturing the deep interests of users. In addition, this framework has strong versatility and can be combined with shallow models such as Logistic Regression (LR) and Factorization Machines (FMs) to form different models and achieve optimal performance. The extended experiment is conducted on two large-scale datasets, AVAZU and SafeDrive, and the experimental results show that the performance of the proposed model is superior to that of existing baseline models.

关键词： recommendation algorithm Click-through rate Implicit high order feature Attention network

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：