检索结果-内蒙古大学图书馆

11th International Conference on Machine Learning and Computing (ICMLC)

作者： Chai, Yuan Li, Dong Wu, Yuchen Qinghai Univ Xining Qinghai Peoples R China Southwest Petr Univ Chengdu Sichuan Peoples R China

ISBN: (纸本)9781450366007

In the new technical background, more and more goods appear in front of the user. Unfortunately, users are increasingly easy to get lost in the massive commodity information. In order to improve the users experience of mobile e-commerce, combined with the advanced technology of large data age, there have been many recommendation algorithms for mobile e-commerce. All of this recommendation algorithms based on customer interests, sales and other different considerations, in order to achieve a purposeful and efficient screening recommendation information. In many ways of thinking, the user reputation as a new era of the spirit of the contract reflects the important value. In this paper, we focus on the current technical situation, designing a set of scoring algorithm through the analysis of the previous behavioral data modeling. After this algorithm, from different customer categories of different needs to start using the mainstream for the score of the business recommendation algorithm algorithm to achieve. And the results are compared with the expected analysis in order to obtain the current algorithm in the credibility of the algorithm under the improvement and adjustment. Finally, we will design a set of recommendation algorithm based on the reputation standards.

关键词： recommendation algorithm collaborative filtering reputation

来源：评论

学校读者我要写书评

暂无评论

Product recommendation algorithm Combining Network Structure and Text Attributes

Product Recommendation Algorithm Combining Network Structure...

引用

IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)

作者： Hu, Wen Ge, Changshun Harbin Univ Commerce Sch Comp & Informat Engn Harbin Peoples R China Harbin Univ Commerce Heilongjiang Prov Key Lab Elect Commerce & Inform Harbin Peoples R China

ISBN: (纸本)9781728181172

Most traditional recommendation algorithms rely on common scoring items between users when making product recommenda-tions. The data is highly sparse and the recommendation effect is not good. Therefore, an improved collaborative filtering recom-mendation algorithm is proposed. Based on user purchase rec-ords, the algorithm uses a representation learning method to construct a user product network, obtains the low-dimensional em-bedded semantic relationship between users and product no-des, and uses cosine similarity to measure the semantic similarity between products. Then, according to the hidden Dirichlet topic distribution model, the topic features of the products are obtained, and the cosine similarity is used to calculate the similarity of the topic features between the products. The linear fusion method is adopted to effectively alleviate the data sparse problem and improve the recommendation performance. Through Amazon product reviews Data set to verify the effectiveness of the recommendation algorithm.

关键词： Representation learning semantic relationship Latent dirichlet allocation topic feature cosine similarity recommendation algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved Collaborative Filtering recommendation algorithm Based on Weibo Content 39

Improved Collaborative Filtering Recommendation Algorithm Ba...

引用

39th Chinese Control Conference (CCC)

作者： Xue, Juntao Ma, Ruohan Zhao, Yunfeng Hei, Junjie Tianjin Univ Sch Elect Automat & Informat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9789881563903

Taking Weibo content as the research object and aiming at the disadvantages of only popular recommendation for Weibo, we propose personalized recommendation scheme. The improved user-based collaborative filtering algorithm is used to give a recommendation list. In this paper, the keywords extraction technology is used to extract the keywords of Weibo content as the project, and the results of the sentiment analysis algorithm are used as the scores to form the project score matrix. Then, the similarity of the item and the score are calculated by the cosine similarity and the Jaccard coefficient, respectively, the final similarity result is obtained by weighted fusion. The experimental results show that the improved algorithm proposed in this paper can effectively solve the problem that the microblog content has no scoring system, which helps to improve the performance of the recommendation system and obtain better recommendation results.

关键词： Weibo recommendation algorithm Similarity Calculation

来源：评论

学校读者我要写书评

暂无评论

An Improved News recommendation algorithm Based on Text Similarity 3

An Improved News Recommendation Algorithm Based on Text Simi...

引用

3rd International Conference on Smart BlockChain (IEEE SmartBlock)

作者： Gao, Yihang Zhao, Hui Zhou, Qian Qiu, Meikang Liu, Meiqin Henan Univ Henan Int Joint Lab Theories & Key Technol Intell Kaifeng Peoples R China Henan Univ Educ Informat Technol Lab Kaifeng Peoples R China Yellow River Water Conservancy Vocat & Tech Coll Art Dept Kaifeng Henan Peoples R China Texas A&M Univ Commerce Dept Comp Sci Commerce TX 75428 USA Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

ISBN: (纸本)9781665440738

With the advent of the data age, the public has been facing the problem of information overload. recommendation algorithms are an effective way to solve this problem. At present, a large number of recommended algorithms adopt the following two ideas: content-based text similarity algorithm and user-based collaborative filtering algorithm. Researchers have developed a distributed collaborative recommendation protocol based on blockchain. However, these algorithms ignore the characteristics of the news industry itself. Just adopting the above ideas will inevitably lead to many internet public opinion problems. Therefore, this paper proposes an improved N-TF-IDF algorithm, which is more suitable for the news industry, and can control the outbreak of negative public opinion, and has a positive effect on stabilizing internet public opinion. Through the verification of the experimental data set, the algorithm is superior to the traditional information retrieval and text mining technology TF-IDF in both the time dimension and the emotional dimension, and this algorithm is not affected by citizens' privacy rights.

关键词： Information Overload recommendation algorithm Collaborative Filtering algorithm Privacy-Preserving TF-IDF Blockchain

来源：评论

学校读者我要写书评

暂无评论

Music recommendation algorithm based on Contex-tual Semantics

International Core Journal of Engineering

引用

International Core Journal of Engineering 2021年第7期7卷 102-110页

作者： Yang Zhou Cuiju Luan

User listening to songs is sequential, while traditional music recommendation algorithms do not con-sider the relationship between listening behaviors and their context, so a music recommendation algo-rithm based on contextual semantics is proposed. Different from the traditional collaborative filtering recommendation algorithm based on user-scoring matrix, this algorithm abstracts the listening records of different users into time-series text sequences, and uses natural language models to capture songs in the process of song conversion. Context semantic relations. For quantized songs, it can be mapped to a high-dimensional space, and the similarity relationship can be obtained by calculating the distance between the songs. For the user, through the user's music listening records, the user can be portrayed to the same dimensional space as the song. Similarly, by calculating the distance between the user and the song, the similarity between the user and the song can be obtained, so as to achieve the purpose of recommendation. Compared with traditional recommendation algorithms, this algorithm can not only capture contextual semantic relationships that traditional recommendation algorithms cannot capture, but also has better overall top-n recommendation performance than traditional algorithms.

关键词： Word2vec recommendation algorithm Music recommendation Contextual Semantic

来源：评论

学校读者我要写书评

暂无评论

Collaborative Filtering recommendation algorithm Based on Bisecting K-means Clustering

Collaborative Filtering Recommendation Algorithm Based on Bi...

引用

International Symposium on Artificial Intelligence and Robotics

作者： Liu, Jia Kang, Xin Nishide, Shun Ren, Fuji Tokushima Univ Sch Informat Fac Engn Tokushima Japan

ISBN: (数字)9781510639690

ISBN: (纸本)9781510639690

The traditional collaborative filtering recommendation algorithm has the problem of data sparsity and expansibility. Aiming at this problem, and improved bisecting k-means collaborative filtering algorithm *** algorithm first removes unrated items in the rating data matrix based on the Weighted Slope One algorithm preprocessing to reduce its sparsity. Then the preprocessed rating data is clustered based on the bisecting K-means algorithm, which reduces the nearest neighbor search space of the target user by assembling similar objects, thereby improving the algorithm's expansibility. Finally, use the recommendation algorithm to generate the final *** results show that the improved bisecting k-means algorithm improves the recommendation effect.

关键词： Collaborative filtering recommendation algorithm Weighted Slope One Bisecting k-means clustering

来源：评论

学校读者我要写书评

暂无评论

The application of the matrix decomposition recommendation algorithm based on attribute coupling is studied in the internet of things

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第5期37卷 6081-6089页

作者： Li, Xiaohong Li, Maolin Shaoyang Univ Prov Key Lab Informat Serv Rural Area Southwester Shaoyang Hunan Peoples R China Shaoyang Univ Coll Informat Engn Shaoyang Hunan Peoples R China

Traditional information recommendation system using only the user's score is calculated and recommended, although to a certain extent, can obtain the implied characteristics of users or resources, but the lack of enough semantic interpretation, affecting the effects of recommendation. This article studied and analyzed the recommendation based on attribute coupled matrix decomposition algorithm in the application of Internet of things, on the foundation of the matrix decomposition model successively introduced global offset and time offset, in order to improve the prediction accuracy and the quality is recommended. In this paper, the algorithm is proved by experiment and the prediction accuracy of the algorithm is improved.

关键词： Property coupling matrix decomposition recommendation algorithm internet of things

来源：评论

学校读者我要写书评

暂无评论

Research and application of recommendation algorithm based on bidirectional attention model

Research and application of recommendation algorithm based o...

引用

2021年第四届算法、计算和人工智能国际会议

作者： Jiahua Wan Chengrui Ji Yiwen Zhang School of Big Data and Artificial Intelligence Anhui Xinhua University School of Artificial Intelligence and Big Data Hefei University

Since the beginning of the 20 th century, with the continuous development of computer technology, more and more people began to use the convenience of computer to improve efficiency, but just because a large number of Internet users continue to increase, there is also a situation of information overload, which will lead to some problems, such as the huge amount of data, the extraction and utilization of effective information will increase Difficulties. The second is the previous recommendation algorithm, most of which predict through the score, but if only through the score, it will not make full use of other data information in the data *** order to make the experimental results more convincing, this experiment uses a recommendation algorithm based on two-way attention model. First, the movie attributes and user attributes are processed by Convolutional Neural Network(CNN), and then through the full connection layer and the built attention model, effective information is obtained. Finally, the predicted score is generated and compared with the real score, and the final result is obtained *** experiment uses the movielens data set, by changing the parameters to affect the experimental results, so as to determine the final value of each parameter. This experiment is a comparative experiment, the recommendation algorithm with two-way attention model is compared with many previous algorithms, and the final conclusion is drawn. The experimental results are expressed by RMSE and MAE. According to the index results, the recommendation algorithm proposed in this experiment has better performance.

关键词： Index Attention model recommendation algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Social Situation Analytics-Based recommendation algorithm for Multimedia Social Networks

引用

IEEE ACCESS 2019年 7卷 117749-117760页

作者： Zhang, Zhiyong Sun, Ranran Choo, Kim-Kwang Raymond Fan, Kefeng Wu, Wei Zhang, Manhua Zhao, Changwei Henan Univ Sci & Technol Informat Engn Coll Luoyang 471023 Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA China Elect Standardizat Inst Beijing 100007 Peoples R China Skyworth Grp Co Ltd Shenzhen 518057 Peoples R China Shenzhen Chuangwei RGB Elect Co Ltd Shenzhen 518108 Peoples R China

Due to the increasing popularity of multimedia social networks (MSNs), the ability to mine users' interests in different contexts on such networks is crucial in recommendation systems. It is, however, challenging to mine users' current preferences based on session on MSNs. In this study, we propose a novel recommendation algorithm based on both social situation analytics and collaborative filtering for session-based recommendation. Specifically, the algorithm predicts the rating for target users based on their nearest neighbors and historical behaviors. First, for the purpose of mining users' current intentions, the session-level behavioral sequences of target user are analyzed based on SocialSitu (t). Then, the recommended contents are generated for target users based on their behaviors and perceived session-based intentions and identity. We evaluate the performance of the proposed algorithm using real-world social media dataset from Shareteches. Findings demonstrate that our algorithm outperforms two classical algorithms and a state-of-the-art method.

关键词： Multimedia social networks situation analytics behavior analysis recommendation algorithm prototype

来源：评论

学校读者我要写书评

暂无评论

Collaborative filtering recommendation algorithm integrating time windows and rating predictions

引用

APPLIED INTELLIGENCE 2019年第8期49卷 3146-3157页

作者： Zhang, Pengfei Zhang, Zhijun Tian, Tian Wang, Yigui Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250001 Shandong Peoples R China

This paper describes a new collaborative filtering recommendation algorithm based on probability matrix factorization. The proposed algorithm decomposes the rating matrix into two nonnegative matrixes using a predictive rating model. After normalization processing, these two nonnegative matrixes provide useful probability semantics. The posterior distribution of the real part of the probability model is calculated by the variational inference method. Finally, the preferences for items that users have not rated can be predicted. The user-item rating matrix is supplemented by a preference prediction value, resulting in a dense rating matrix. Finally, time weighting is integrated into the rating matrix to construct the 3D user-item-time model, which gives the recommendation results. According to experiments using open Netflix, MovieLens, and Epinion datasets, the proposed algorithm is superior to several existing recommendation algorithms in terms of rating predictions and recommendation effects.

关键词： Probability matrix factorization Time weighting Probability model recommendation algorithm

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：