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IEEE International Symposium on Circuits and Systems

作者： Bin Sun Hua Tan Dongxiao Yang Yu Zhang Yefei Wu Xinyu Jin Coege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. ChinCoege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. ChinCoege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. ChinCoege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. ChinCoege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. ChinCoege of Inforion Science Eecronic Zhejing Universiy Hngzhou P. R. Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

"Electronic system design" is an important course closely related to electronic design competition. A wide range of knowledge modules are involved in the class, but some modules are not studied by students. Due to the constraints of classroom time, they need to preview independently before class under the guidance of teachers. We design a recommendation system based on improved collaborative filtering algorithm, so that students can learn by themselves according to the learning resources pushed by teachers. To increase the accuracy of collaborative filtering algorithm, the user's attribute similarity is combined with the traditional collaborative filtering recommendation algorithm to improve the cold start problem and data sparsity of the algorithm. Then we build an experimental teaching platform of "Electronic System Design" curriculum to achieve automatic recommendation of experimental learning resources under teachers' guidance.

关键词： learning resource recommendation collaborative filtering algorithm similarity recommendation system

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Commercial New Media Advertising Recommendation algorithm Based on Data Analysis

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Procedia Computer Science 2024年 247卷 537-544页

作者： Yongkang Zhang Sihan Fu Taylor's University Selangor 200127 Malaysia DAWN Education Consulting Co. Ltd. Foshan 528200 Guangdong 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.

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

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Contextual collaborative filtering Recommendation Model Integrated with Drift Characteristics of User Interest

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2021年 11卷

作者： Guo, Feipeng Lu, Qibei Zhejiang Gongshang Univ Modern Business Res Ctr Hangzhou Peoples R China Zhejiang Gongshang Univ Sch Management & Business Hangzhou Peoples R China Zhejiang Int Studies Univ Sch Cross Border E Commerce Hangzhou Peoples R China Zhejiang Int Studies Univ Sch Sci & Technol Hangzhou Peoples R China

User interest will drift with the change of context, cognitive psychology, and so on, which leads to inaccurate recommendation. In order to address this issue and the traditional recommendation problems such as cold start and data sparsity, this study proposed a novel contextual collaborative filtering recommendation model. First, the reasons for drift of user interest from the perspective of motivation were analyzed, and this study designed a mechanism based on Maslow's hierarchy of needs to analyze the information category and information behavior corresponding to the hierarchy of users' needs. Then, a novel user interest determination algorithm was proposed based on ontology and hidden Markov. Second, this study introduced the concept of user activity and proposed a user activity computational method integrated with context to solve the cold start and data sparsity problems. Finally, the research proposed a dynamic collaborative filtering recommendation algorithm integrated with user activity to diversify the content of candidate recommendation selectively. By monitoring users' feedback and the learning rules of interest drift, this method can discover drift of user interest and make some adaptation actively. The experimental results showed that this model, which integrates with the drift characteristics of user interest, can effectively improve the adaptability to the drift of user interest, and that it has higher accuracy compared with other recommendation methods.

关键词： Contextual Recommendation Maslow's Hierarchy of Needs User Activity Interest Drift collaborative filtering algorithm Hidden Markov

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Design of Personalized Recommendation System for Teaching Resources Based on Cloud Edge Computing

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Procedia Computer Science 2024年 243卷 826-833页

作者： Xuemin Chen Guangdong Innovative Technical College Dongguan 523960 China

With the continuous development of cloud computing and edge computing technologies, the education sector is gradually applying these technologies to enhance the management and utilization efficiency of teaching resources. Therefore, to address the issue of information overload mentioned above, it is necessary to establish a personalized recommendation system based on user needs, preferences, and other information, recommending products, information, and resources that may be of interest to users. This can not only save users search time, but also alleviate the problem of information overload to some extent. On this basis, this article discussed a new content oriented recommendation method: constructing a user interest feature vector resource association matching model, and analyzing it to achieve recommendation of similar resources. The experimental results showed that the MAE (Mean Absolute Error) value of personalized recommendation based on CF (collaborative filtering) algorithm was below 0.8, which was smaller than other algorithms, indicating high accuracy of recommendation based on CF algorithm.

关键词： Teaching Resources Personalized Recommendation System Cloud Edge Computing collaborative filtering algorithm

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Design and Development of Intelligent Learning System for University Innovation and Entrepreneurship Based on Knowledge Visualisation

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第3期23卷

作者： Feng, Bibo Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

To build a comprehensive learning resource system covering learning process, learning users, learning resources and other aspects, an intelligent learning system for innovation and entrepreneurship in universities was constructed and designed combined with knowledge visualisation theory. The system includes student information record module, system recommendation calculation module, learning material module, and knowledge visualisation module. The research focuses on the system recommendation calculation module, including the User-based collaborative filtering algorithm (UCF) and the Joint Recommendation (JR) algorithm of the application content recommendation algorithm. The best neighbour number of JR is 20, and the best transmission path length is 2. The recommendation effect is optimal under this setting. The convergence iterations of the training and test set of the joint recommendation algorithm are 80 times and 100 times, respectively. For the training set and test set, the accuracy, precision, sensitivity, recall, running time, error, and other performance evaluation indicators of the joint recommendation algorithm are better than the corresponding values of other recommendation algorithms. The system has a good design effect and experience effect in the application of the online learning platform.

关键词： Knowledge visualisation intelligent learning system collaborative filtering algorithm recommend

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Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Meng, Xiaoyan Inner Mongolia Normal Univ Inner Mongolia Youth Coll Polit Sci Hohhot 010051 Peoples R China Inner Mongolia Normal Univ Youth Coll Polit Sci Hohhot 010051 Peoples R China Inner Mongolia Youth League Sch Hohhtot 010051 Peoples R China

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation *** the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k=10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates c

关键词： Personalized recommendation Matrix factorization algorithm Cross-domain information fusion Data sparsity collaborative filtering algorithm

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Trust Mechanism of Feedback Trust Weight in Multimedia Network

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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2021年第4期17卷 1-26页

作者： Lv, Zhihan Song, Houbing Uppsala Univ Fac Arts Dept Game Design SE-75105 Uppsala Sweden Embry Riddle Aeronaut Univ Dept Elect Engn & Comp Sci Daytona Beach FL 32114 USA

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, a FTWDF ( Feedback Trust Weighted for Data Fusion) is proposed. Second, EEFA (Efficiency unequal Fuzzy clustering algorithm) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of the TMDF (Trust Multiple attributes Decision-making-based data Fusion) algorithm, and 8.3% higher than that of LDTS (Larger Data fusion based on node Trust evaluation in wireless Sensor networks) algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks' lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks' lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes' energy consumption and effectively save nodes' overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for

关键词： Network fuzzy theory trust mechanism collaborative filtering algorithm FTWDF simulation FTWDF-EEFA

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Fast Retrieval Method of Massive Library Literature Resources Based on an Online Hash algorithm

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JOURNAL OF TESTING AND EVALUATION 2023年第3期51卷 1543-1554页

作者： Wang, Huan Harbin Univ Sci & Technol Lib 52 Xuefu Rd Harbin 150040 Heilongjiang Peoples R China

Aiming at the relatively large amount of library literature resources, a fast retrieval method for massive library literature resources based on an online hash algorithm is designed. The Pearson correlation coefficient is used to calculate the correlation between library literature resources, the collaborative filtering algorithm is used to realize the library literature resource mining based on the calculation results, and the least squares method is used to filter the nonsignificant data features in the mining results. Construct a Hash-B-tree to search the user's library literature resources on a certain day, design a hash function lookup table to manage inactive metadata, and use fuzzy theory to simulate data with similar characteristics in the data divergence measurement sample, minimize the divergence of the sample data, and finally visualize the retrieval results based on the subject hierarchical relationship. The experimental results show that this method has the advantages of higher retrieval result accuracy, faster retrieval efficiency, and higher comprehensiveness of retrieval results.

关键词： online hash algorithm resource retrieval least squares method collaborative filtering algorithm Hash-B-tree

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Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

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BMC MEDICAL GENOMICS 2021年第SUPPL 3期14卷 225-225页

作者： Nguyen, Van Tinh Le, Thi Tu Kien Nguyen, Tran Quoc Vinh Tran, Dang Hung Hanoi Univ Ind Fac Informat Technol Hanoi Vietnam Hanoi Natl Univ Educ Fac Informat Technol Hanoi Vietnam Univ Sci & Educ Univ Da Nang Fac Informat Technol Da Nang Vietnam

Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

关键词： Infer miRNA-disease associations miRNA-disease-lncRNA tripartite graph collaborative filtering algorithm Resource allocation algorithm Recommender systems

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Integration of Intelligent Drug Systems and Personalized Medicine: Applications and Prospects 23

Integration of Intelligent Drug Systems and Personalized Med...

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Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science

作者： Xiao Shao College of Biological and Pharmaceutical Engineering Jilin Agricultural Science and Technology University 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： collaborative filtering algorithm Intelligent drugs Personalized medicine doctor recommendation method

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