A Novel Personalized Recommendation Algorithm of Collaborative Filtering Based on RFM Model by Ning Zhang, Chongrui Fan, Yan Zhang

“…At last, the improved algorithm was applied to a recommendation system with personalized recommendation function. …”
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Knowledge graph and data application:intelligent recommendation by Jing ZHOU, Ximin SUN, Xiaokun YU, Xinning BIAN

“…Knowledge graph technology is an emerging internet technology with good forward-looking and strong technological advancement,and has broad application prospects.Knowledge graph technology provides the ability to analyze problems from the “relationship” perspective.It can deeply mine data,transform natural language into computer language,and display the value of data to the greatest extent.It can serve intelligent search,intelligent recommendation,risk early warning,intelligent operation,intelligent customer service,public opinion monitoring,equipment early warning and other businesses,greatly improving the production efficiency of enterprises.The comprehensive clustering algorithm,SVD decomposition algorithm,commodity-based collaborative concerns recommendation algorithm,user-based collaborative concerns recommendation algorithm and commodity similarity algorithm were studied.Combined with recommendation strategy,an intelligent recommendation system based on knowledge graph data application was proposed.It proves the feasibility and deployability of the data application with knowledge graph as the basic tool,which can fully meet the user’s demand for information on the internet platform.…”
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Spectrum knowledge graph: an intelligent engine facing future spectrum management by Jiachen SUN, Jinlong WANG, Guoru DING, Jin CHEN, Yuping GONG

“…To solve the issues of simple representations on spectrum situation, much dependence on artificial experience in manual management and low efficiency and accuracy in the current spectrum management, meeting the requirements of automation, precision and real time for future spectrum management, the theory and technology of knowledge graph were introduced into spectrum management.The definition of spectrum knowledge graph, the knowledge schema it depends on and its representation in the form of triples were proposed.The intelligent spectrum management framework based on spectrum knowledge graph, consisting of the graph layer, the equipment layer and the scenario layer, was constructed.Typical applications based on spectrum knowledge graph were discussed, including the recommendation system for spectrum usage, the search engine on spectrum management, and question answering for spectrum management.Experiments demonstrate the spectrum knowledge graph can play a role of guidance by spectrum knowledge in spectrum usage recommendation.…”
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Rough decision rules extraction and reduction based on granular computing by Hong-can YAN, Feng ZHANG, Bao-xiang LIU

“…Rule mining was an important research content of data mining,and it was also a hot research topic in the fields of decision support system,artificial intelligence,recommendation system,etc,where attribute reduction and minimal rule set extraction were the key links.Most importantly,the efficiency of extraction was determined by its application.The rough set model and granular computing theory were applied to the decision rule reduction.The decision table was granulated by granulation function,the grain of membership and the concept granular set construction algorithm gener-ated the initial concept granular set.Therefore,attribute reduction could be realized by the distinguish operator of concept granule,and decision rules extraction could be achieved by visualization of concept granule lattice.Experimental result shows that the method is easier to be applied to computer programming and it is more efficient and practical than the existing methods.…”
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A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network by Qiang Sun, Leilei Shi, Lu Liu, Zixuan Han, Liang Jiang, Yan Wu, Yeling Zhao

“…Experiments are conducted on the MovieLens training dataset, and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.…”
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Lower bounds for quantum-inspired classical algorithms via communication complexity by Nikhil S. Mande, Changpeng Shao

“…We mainly focus on lower bounds for solving linear regressions, supervised clustering, principal component analysis, recommendation systems, and Hamiltonian simulations. For those problems, we prove a quadratic lower bound in terms of the Frobenius norm of the underlying matrix. …”
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A Cloud API Personalized Recommendation Method Based on Multiple Attribute Features and Mashup Requirement Attention by Limin Shen, Yuying Wang, Chengyu Li, Zhen Chen

“…In current mashup-oriented cloud API recommendation systems, insufficient attention to personalized development requirements remains a common issue, particularly regarding developers’ needs for attributes such as functionality similarity and complementarity. …”
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Design and Implementation of Attention-Based CR System in the Context of Big Data by Zheyi Wang, Yanli Kuang, Xin Lyu

“…The recommendation accuracy of traditional product recommendation systems is insufficient. Therefore, an improved deep factor decomposition machine algorithm combining adaptive regularization and attention mechanisms is proposed, and big data components are integrated to enable the algorithm to support more data input types. …”
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Evaluation of the Use of Artificial Intelligence in Teaching Learning Evaluation Courses by Muhamad Raffi Fadliansyah, Rian Dwiyanto, Nur Syamsiyah Nur Syamsiyah

“…By utilizing AI technology, such as chatbots, learning recommendation systems, and data analysis tools, teaching this course is expected to increase the effectiveness, efficiency, and engagement of students in the learning process. …”
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Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms by Shuai Zhang, Hongyan Liu, Jun He, Sanpu Han, Xiaoyong Du

“…Therefore, a personalized recommendation system is important for live streaming platforms. …”
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Course Recommendations in Online Education Based on Collaborative Filtering Recommendation Algorithm by Jing Li, Zhou Ye

“…In this paper, a personalized online education platform based on a collaborative filtering algorithm is designed by applying the recommendation algorithm in the recommendation system to the online education platform using a cross-platform compatible HTML5 and high-performance framework hybrid programming approach. …”
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Leveraging Deep Learning for Personalized Book Recommendations: A Big Data Algorithm Combining Capsule Networks and Attention Mechanisms by Jiali Liao, Tianxiang Li

“…This study provides new insights for developing more accurate and efficient big data recommendation systems in library services and beyond.…”
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New Similarity of Triangular Fuzzy Number and Its Application by Xixiang Zhang, Weimin Ma, Liping Chen

“…Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users’ comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher.…”
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Sistem Rekomendasi Dua Arah untuk Pemilihan Dosen Pembimbing Menggunakan Data Histori dan Skyline View Queries by Global Ilham Sampurno, Annisa Annisa, Sony Hartono Wijaya

“…This research develop a two-way recommendation system, considering both supervisor’s and student’s preferences using skyline view queries. …”
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Recommendation Algorithm in Double-Layer Network Based on Vector Dynamic Evolution Clustering and Attention Mechanism by Jianrui Chen, Zhihui Wang, Tingting Zhu, Fernando E. Rosas

“…The purpose of recommendation systems is to help users find effective information quickly and conveniently and also to present the items that users are interested in. …”
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A homophilic and dynamic influence maximization strategy based on independent cascade model in social networks by Gang Wang, Shangyi Du, Yurui Jiang, Xianyong Li

“…Influence maximization (IM) is crucial for recommendation systems and social networks. Previous research primarily focused on static networks, neglecting the homophily and dynamics inherent in real-world networks. …”
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Efficient Preference Clustering via Random Fourier Features by Jingshu Liu, Li Wang, Jinglei Liu

“…., cosine and sine ) are sampled from a distribution independent from the training sample set, to cluster preference data which appears extensively in recommender systems. Firstly, we propose a two-stage preference clustering framework. …”
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Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining by Chaohua Fang, Qiuyun Lu

“…In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. …”
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Real-Time Adaptive Tractor Ride Comfort Adjustment System Based on Machine Learning Method by Hu Tian, Anping Ji

“…To address this gap, we propose a real-time recommendation system based on Internet of Things (IoT) and Machine Learning (ML) to enhance the driving comfort of agricultural tractors. …”
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IoT-based approach to multimodal music emotion recognition by Hanbing Zhao, Ling Jin

“…This study presents an effective solution for multimodal emotion recognition, demonstrating its broad potential in applications such as intelligent emotional interaction and music recommendation systems.…”
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