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Personalised recommendation algorithm of network resources based on collaborative knowledge map

INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第2期16卷 159-174页

作者： Liu, Xiaofei Zhong, Shaohui Open Univ Guangdong Sch Artificial Intelligence Guangzhou 510091 Peoples R China Hunan Radio & Televis Univ Informat Coll Changsha 410078 Peoples R China

In order to overcome the problems of wide coverage of network resources and confusion of semantic information, resulting in low precision of recommendation and low recall rate, a personalised recommendation algorithm based on collaborative knowledge map is proposed. The collaborative knowledge map is introduced into the research field of network resources, the knowledge features of network resources are extracted specifically. The collaborative knowledge ontology database of network resources field is constructed according to the entity division relationship in the research field. Entities and relationships are extracted from network resources, to build a knowledge map based on relational database, quantify the network resources in the knowledge map, extract semantic information of network resources with collaborative filtering algorithm, so as to achieve the purpose of personalised recommendation of network resources. The simulation results show that the recommended precision is more than 98%, and the recall rate and comprehensive evaluation index are better.

关键词： collaborative knowledge map network resources special extraction collaborative filtering algorithm personalised recommendation relational database comprehensive evaluation index

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Online Teaching Design and Evaluation of Innovation and Entrepreneurship Courses in the Context of Education Internationalization

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第2期14卷 729-738页

作者： Xing, Chengshe Yiwu Ind & Commercial Coll Sch Foreign Studies Yiwu 322000 Peoples R China

In the context of the internationalization of education nowadays, courses in innovation and entrepreneurship have been strongly promoted, and the content and number of topics, etc. of this type of courses are rapidly climbing. In order to enable target users to quickly select courses that they may be interested in, one changed collaborative filtering algorithm based on a multi-feature ranking model is used to extract and rank the features of online courses based on several factors, and then combine the collaborative filtering algorithm to recommend them to users. The results of experiment show that the numerical valuation of accuracy rate and recall rate of the improved algorithm are more than those of the other algorithm with different conditions, and in most cases higher than those of the LDA algorithm, and the user's evaluation of the recommendation effect also has the highest rating value of the improved algorithm, with the ratings of 4.3, 4.7 and 4.4 in the three groups, and the overall average score is 4.47, indicating that the improved algorithm has significant optimization performance and is suitable for teaching innovation and entrepreneurship in online courses.

关键词： -Online course collaborative filtering algorithm Ranking model

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An Enhanced Recommendation algorithm Based on Modified User-Based collaborative filtering 4

An Enhanced Recommendation Algorithm Based on Modified User-...

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4th IEEE International Conference on Computer and Communication Systems (ICCCS)

作者： Lumauag, Ramil G. Sison, Ariel M. Medina, Ruji P. Technol Inst Philippines Grad Sch Program Quezon City Philippines Emilio Aguinaldo Coll Sch Comp Studies Manila Philippines

ISBN: (纸本)9781728113227

With the huge amount of information available on the Internet, recommendation systems gained popularity over the years. Traditional recommendation algorithm usually uses collaborative filtering to determine user and item similarity. However, data sparsity and overfitting affects the accuracy of the recommendation systems that lead to poor recommendation quality. This paper presents an enhanced recommendation algorithm based on modified user-based collaborative filtering to overcome the problem and improve the recommendation quality. The enhanced algorithm was compared to the traditional algorithm using the MovieLens dataset and evaluates its accuracy and performance using the Root Mean Square Error (RSME), Precision and Recall. The experimental results show that the enhanced algorithm outperforms the traditional algorithm and improves the accuracy of the recommendation.

关键词： collaborative filtering algorithm recommendation algorithm recommender systems user-based collaborative filtering

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New Media Content Creation and Recommendation Based on Artificial Intelligence 14th

New Media Content Creation and Recommendation Based on Artif...

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14th International Conference on Frontier Computing, FC 2024

作者： Wang, Luying Jiang, Yujie Academy of Film and Television Arts Hebei Institute of Communications Hebei Shijiazhuang051430 China

ISBN: (纸本)9789819627974

With the rapid development of new media and the rise of self media, the copyright of content belongs to users, and ordinary users can also create corresponding content. However, the creation of popular science works is a very scientific and rigorous work that cannot be completed by ordinary users. The output of high-quality popular science content still relies on professional editors from popular science media organizations or relevant research institutions, as well as the scientific and technological departments of authors of popular science publications. Therefore, how to create timely and professional popular science content has become a new topic. This article explored the creation and recommendation of new media content based on artificial intelligence. The Naive Bayes algorithm was used to classify new media content, and mixed similarity was used to calculate the similarity between users and content, achieving the creation and recommendation of new media content. The experimental results showed that the recommendation accuracy of the hybrid recommendation algorithm was higher than that of the collaborative filtering algorithm, and the difference in accuracy between the two was at least 2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： collaborative filtering algorithm Creation and Recommendation Naive Bayesian algorithm New Media Content

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Commercial New Media Advertising Recommendation algorithm Based on Data Analysis 11th

Commercial New Media Advertising Recommendation Algorithm Ba...

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11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Zhang, Yongkang Fu, Sihan Taylor's University Selangor 200127 Malaysia DAWN Education Consulting Co. Ltd. Guangdong Foshan528200 China

With the rise of new media, people's understanding of advertising has also changed. Traditional advertising methods are difficult to attract people's attention, and the information conveyed by commercial advertising is becoming increasingly diverse. In the new media environment, users must rely on various media to have a more comprehensive understanding of advertising. The development of commercial advertising relies on various new media. New media is a new carrier and channel that creates conditions for the leapfrog development of advertising. Consumers can only have a more comprehensive understanding of an advertising brand through the joint integration of multiple media. Therefore, in the context of new media, based on consumer awareness and consumption habits of advertising, it is necessary to improve the efficiency of advertising design and dissemination, in order to better meet consumer needs. The commercial new media advertising recommendation technology based on CF (collaborative filtering) algorithm has great potential and advantages. Through personalized recommendations, the click through rate and conversion rate of advertisements can be improved, bringing better advertising effects to enterprises. In the future, with the continuous development of data mining and artificial intelligence technology, advertising recommendation technology based on collaborative filtering algorithms would be further improved and applied, bringing greater development space for commercial new media advertising. The experimental results showed that after implementing the recommendation algorithm, the attention level of the advertisement was above 0.7, and the click through rate was as high as 0.8 or above. Due to the high accuracy and personalization of recommendation algorithms, the advertisements seen by users are more in line with their interests and needs, thereby increasing their attention and click through rate towards advertisements. © 2024 The Authors. Published by Elsevi

关键词： Advertising Recommendation algorithm Business New Media collaborative filtering algorithm Data Analysis

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Research on New Economic Marketing Model System Based on Computer Intelligent Recommendation algorithm 3

Research on New Economic Marketing Model System Based on Com...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Jiang, Yuanyuan Guangxi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798350380989

In this paper, the knowledge based on decision tree and user context is combined to give a financial product portfolio recommendation method based on matrix decomposition. This project aims to improve the recommendation accuracy of personal finance products. Then, the matrix decomposition method of scenario preference is introduced to obtain a preliminary list of financial product suggestions. The initial recommendation list is filtered twice according to the preferences of users in specific situations obtained by the classification model to get the final recommendation result. For each person's different choices in a specific situation, the initial recommendation list is filtered in a second round to obtain the final recommendation result. Compared with the conventional collaborative filtering algorithm, matrix decomposition algorithm and Baseline algorithm, the situational cognition improves the recommendation accuracy. This algorithm improves the performance of the recommendation system. © 2024 IEEE.

关键词： classification model collaborative filtering algorithm Economic marketing financial product recommendation system matrix decomposition situational awareness

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Scenic Spot Recommendation Method Integrating Knowledge Graph and Distance Cost 32nd

Scenic Spot Recommendation Method Integrating Knowledge Grap...

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Shen, Yue Zhu, Xiaoxu Soochow Univ Sch Comp Sci & Technol Suzhou Peoples R China

ISBN: (纸本)9783031441943;9783031441950

Aiming at the problem that the traditional collaborative filtering algorithm only considers external ratings and cold start when recommending attractions, this paper proposes an attraction recommendation algorithm integrated with knowledge graph. Firstly, we construct a user rating matrix based on user ratings and the number of reviews, and calculate the similarity of attractions. Then, we use TransR model to train the semantic vector matrix of attractions, and use cosine similarity formula to calculate the semantic similarity of attractions. Finally, the two similarities are fused and applied to ALS matrix factorization. At the same time, in order to make the model pay attention to user preferences and take into account the distance elements in the scenic spots for recommendation, the distance cost is integrated in the loss function. Experimental results on the scenic spots dataset show that the proposed algorithm is better than the traditional method in scenic spots recommendation.

关键词： collaborative filtering algorithm Knowledge graph Distance cost Scenic spot recommendation

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Integration of Intelligent Drug Systems and Personalized Medicine: Applications and Prospects 4

Integration of Intelligent Drug Systems and Personalized Med...

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4th International Symposium on Artificial Intelligence for Medicine Science (ISAIMS)

作者： Shao, Xiao Jilin Agr Sci & Technol Univ Coll Biol & Pharmaceut Engn Jilin 132000 Jilin Peoples R China

ISBN: (纸本)9798400708138

Integrating intelligent drugs and personalized medicine is a transformative approach in healthcare, combining AI, genomics, and nanotechnology. This fusion aims to develop tailored medications for individual patients, utilizing dynamic drug delivery systems, targeted therapies, and dose control. It holds immense potential for improving patient outcomes and revolutionizing healthcare delivery. This article uses the CTM topic model to optimize the existing online doctor expert recommendation model. Firstly, the problem topic probability distribution is obtained based on the health questions raised by patients, and then the doctor topic probability distribution is obtained based on all the questions answered by doctors in history. Finally, a list of doctors with high topic similarity is recommended to patients. In the experimental stage, data from the Allergy Department of the Good Doctor Online Light Consultation Module was collected and processed, followed by modeling and testing. The results confirmed that the doctor recommendation method proposed in this article is more efficient than the existing recommendation methods in the department.

关键词： Intelligent drugs Personalized medicine collaborative filtering algorithm doctor recommendation method

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Design of Information Learning Platform Management System in Network Security 1

Design of Information Learning Platform Management System in...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Han, Xue Hou, Guanyu Jilin Agricultural Science and Technology University Editorial Department of Journal Jilin China School of Mechanical and Electrical Engineering and Automation Harbin Institute of Technology Guangdong Shenzhen China

ISBN: (纸本)9798350316469

In recent years, with the rapid development of modern technology and the continuous promotion of IT, IT has been widely applied in modern performing arts. Information management has become the most practical and effective method and means in the management of performance art training. However, with the exponential growth of various data volumes, the demand for computing and processing capabilities, as well as speed, for massive amounts of data and information is also increasing. This paper proposed a platform based network security solution, established a complete network security policy system, and conducted experiments on users based on three different collaborative filtering recommendation algorithms. The results showed that the Jaccard-CF algorithm proposed in this paper had the highest mean absolute error value of 0.752, and its prediction results were more accurate than the other two types of algorithms. © 2023 IEEE.

关键词： collaborative filtering algorithm Information Technology Learning Platform Management system Network Security

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Intelligent Movie Recommendation System Based on Hybrid Recommendation algorithms 1

Intelligent Movie Recommendation System Based on Hybrid Reco...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Pu, Qingna Hu, Bin Sichuan University Jinjiang College Sichuan Province Meishan China

ISBN: (纸本)9798350316469

With the continuous advancement of modern science and technology and the arrival of the artificial intelligence era, the problem of 'information overload' has become increasingly prominent. The emerging recommendation systems not only provide users with an excellent user experience and convenience but also help businesses achieve greater profits. To cater to the personalized needs of users, this study has chosen a movie recommendation system based on a hybrid recommendation algorithm. This system reduces the time users spend on information searching and enhances their search efficiency, aiming to recommend movies that align with their preferences. Our hybrid recommendation algorithm has shown an accuracy rate of 81%, surpassing traditional CB, Item-Based CF, and User-Based CF algorithms. This recommendation system not only shortens the time users take to search for information but also improves their search efficiency. © 2023 IEEE.

关键词： collaborative filtering algorithm hybrid recommendation algorithm movie recommendation system

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