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2015 3rd International Conference on Recent Trends in Materials and Mechanical Engineering (ICRTMME 2015)

作者： Shijin Li Wate He Xibing Wang Ting Shi Yunnan University of Finance Yunnan University Yunnan Business Information Engineering School

With the rapid development of Internet and information technology, the exponential growth of information has attracted a lot of concern thesedays. Big data processing is particularly important. Recommendation system appears a good solution inadequacies of search engines, it is in addition based on keywords entered by the user to obtain information, but also with the user's social circle, as well as search history records, for users personalized recommendations services, and to establish a long and constant user interaction relations, not only improve customer loyalty, but also for the producers to create a good and reliable information platform for big data processing to achieve a win-win.

关键词： Big data Recommendation system collaborative filtering algorithm Big data processing

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Research and Implementation of Personalized Recommendation algorithm for Senior Diet Exercise Based on collaborative filtering 4

Research and Implementation of Personalized Recommendation A...

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

作者： Wang, Xinting Song, Yifan Chen, Wensen Du, Haiyan Su, Xiaohui Wang, Haiyan Beijing Forestry Univ Sch Informat Sci & Technol Beijing Peoples R China Natl Forestry & Grassland Adm Engn Res Ctr Forestry Oriented Intelligent Inform Beijing Peoples R China

ISBN: (纸本)9798400708138

In this paper, machine learning methods are introduced to build a personalized recommendation algorithm based on collaborative filtering for meals and exercises for the elderly to meet their personalized needs for meals and exercises. Firstly, a database was established by combining diet and exercise data and medical knowledge, and then an item-based and user-based recommendation algorithm was studied based on collecting personalized preferences of target users, and finally, different weights were assigned to the three variables ICF, UCF and Initial according to their different importance to obtain a fused personalized diet and exercise recommendation algorithm to achieve Top-n personalized recommendation for users. The results show that when the number of users is 500, the accuracy of the personalized meal and exercise recommendation algorithm is 63.1%, which is better than the item-based collaborative filtering algorithm (59.7%) and the user-based collaborative filtering algorithm (52.5%), and the correctness of the fusion algorithm is higher through verification. In the actual test situation, most users were satisfied with the fusion algorithm (60%), indicating that the personalized dietary exercise recommendation algorithm has high reliability, and the personalized dietary exercise recommendation algorithm has been applied to the recommendation system of dietary exercise for the elderly.

关键词： Meal Recommendation Exercise Recommendation collaborative filtering algorithm Personalization Smart Aging

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Research on the Performance of collaborative filtering algorithms in Library Book Recommendation Systems: Optimization of the Spark ALS Model 2

Research on the Performance of Collaborative Filtering Algor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Chen, Rui Shandong Huayu University of Technology Dezhou253034 China

ISBN: (纸本)9798350317558

Since the rapid development of information technology, the amount of book information stored in university libraries has been increasing. Traditional information retrieval techniques can no longer meet the needs of teachers and students to quickly search and filter information of interest from a massive collection of books. Therefore, in order to better serve university teachers and students, establishing a collaborative filtering recommendation system for university libraries can help push book information more accurately. This article introduces the development process of the Spark model, elaborates on the recommendation algorithm based on the Spark ALS model and its optimization, and studies the collaborative filtering recommendation algorithm based on the ALS model. The algorithm is implemented on a distributed platform, compared to previous single node implementations. The experimental results show that the optimization algorithm based on the Spark ALS model has greatly improved its computational speed, and reduces the loss of hidden factor item attribute information by integrating item similarity in the loss function. At the same time, an interest forgetting function is also introduced in the prediction score obtained from the optimal model. Through experimental comparison, the results show that the optimization algorithm proposed in this paper effectively improves the accuracy of the recommendation system. At the same time, a questionnaire survey was conducted on 250 students who used the system. The survey results showed that the students who participated in the internal testing had a good overall satisfaction with the system, with scores above 3.9 out of 5. The experiment showed that the improvement of the system was significant, and the actual application effect was good. © 2024 IEEE.

关键词： Book recommendation system collaborative filtering algorithm Library optimization Spark ALS model

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Research on Intelligent collaborative filtering algorithm of social network environment 5

Research on Intelligent collaborative filtering algorithm of...

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5th International Conference on Intelligent Systems Design and Engineering Applications (ISDEA)

作者： Guo Du-gang Wu Yong-tang Tian Hui Weifang Univ Sci & Technol Weifang 262700 Shandong Peoples R China

ISBN: (纸本)9781479942626

For the data sparsity and malicious behavior problems of traditional collaborative filtering algorithm, the paper introduces a new kind of social network-based collaborative filtering algorithm. In order to improve the accuracy of the personalized recommendation technology, we first define empty state in the state space of multi-dimensional semi-Markov processes and obtain extended multidimensional semi-Markov processes which is combined with social network analysis theory, and then we get social network information flow model. The model describes the flow of information between the members of the social network. So, we propose collaborative filtering algorithm based on social network information flow model. The algorithm uses social network information and combines user trust with user interest and find nearest neighbors of the target user and then forms a project recommended to improve the accuracy of recommended. Compared with the traditional collaborative filtering algorithm, the algorithm can effectively alleviate the sparsity and malicious behavior problem, and significantly improve the quality of the recommendation system recommended.

关键词： collaborative filtering algorithm Social network Intelligent

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A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

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KYBERNETES 2023年第1期52卷 328-343页

作者： Zhou, Kangqu Yang, Chen Li, Lvcheng Miao, Cong Song, Lijun Jiang, Peng Su, Jiafu Chongqing Univ Technol Coll Mech Engn Chongqing Peoples R China Wuhan Univ Technol Sch Entrepreneurship Wuhan Peoples R China Natl Inst Dev Adm Bangkok Thailand Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing Peoples R China Krirk Univ Int Coll Bangkok Thailand

Purpose - This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities. Design/methodology/approach - First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources. Findings - Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F1, which indicates that the proposed algorithm yields better performance. Originality/value - The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.

关键词： Crowdsourcing Folksonomy Semantic similarity collaborative filtering algorithm

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collaborative filtering Recommendation algorithm Based on User's Comprehensive Information Particle Swarm Optimization and K-means Clustering

Collaborative Filtering Recommendation Algorithm Based on Us...

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2015 International Conference on Industrial Informatics, Machinery and Materials(IIMM 2015)

作者： Vandana MALODE Bhagawat PATIL Department of Electronics J.N.E.C. N-6 CIDCO Department of E&TC Army Institute of Technology

With the thriving era of the Internet, information arises about billions per day. Along with the excessive expansion of the amount of information, "Information Overload" is approaching. As a result, it is inconvenient for users to achieve needed information from various information. It is obvious that recommendation system will be an essential measure to solve the "Information Overload" in the future. Nowadays, the personalized recommendation system has been used widely, for example, e-business, camera and video. As a consequence, a lot of recommendation technologies arise, such as content-based recommendation technology, the collaborative filtering recommendation technology, graph-based technology and so on. What's more, the collaborative filtering recommendation technology is the most mature technology among them. However, with the abrupt increase of the users and items, the collaborative filtering technology is facing challenges of Cold Start and Data Sparsity. The paper aims mainly at Data Sparsity in user-based collaborative filtering algorithm. Based on the user preference particle swarm clustering based on collaborative filtering recommendation algorithm, analyzing users' choices caused by users' features. A collaborative filtering recommendation algorithm based on user's comprehensive information particle swarm clustering is proposed. The algorithm considers user features and user preference information to the user for particle swarm clustering and nearest neighbor set selection. Through the simulation experiments, it shows that the algorithm has a higher accuracy than user preference particle swarm clustering based on collaborative filtering recommendation algorithm.

关键词： Recommendation system collaborative filtering algorithm Particle swarm clustering K-means clustering Cold start data sparsity

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Contractor Recommendation Model Using Credit Networking and collaborative filtering

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BUILDINGS 2022年第12期12卷 2049页

作者： Zhang, Yao Tai, Shuangliang Ye, Kunhui Chongqing Univ Sch Management Sci & Real Estate Chongqing 400045 Peoples R China Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China

The credit of contractors in the construction market directly affects the cooperative intentions of owners. Although previous scholars have attempted to use credit to select appropriate contractors, they have rarely considered the trust relationship between decision-making and former owners. This work introduces and verifies a credit network recommendation model based on a collaborative filtering algorithm. The contractor's credit established based on this model serves as a viable method for owners to select efficient contractors. The application of the model includes relevant information collection, neighbor set formation, contractor's credit evaluation, and recommendation list formation, among which the neighbor set of the owner is used to calculate the comprehensive trust degree of the decision-making owner to the former owner. A time decay function is adopted to correct the difference in the trust relationship between an owner and a contractor introduced over time. To verify the feasibility of this model, an actual scenario was simulated, and the results obtained via simulations were compared and found to be consistent. Thus, a contractor with a high credit can be recommended to the decision-making owner. This approach is crucial for promoting contractors' credit and conducive to the healthy development of the construction market.

关键词： contractor selection collaborative filtering algorithm credit network

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Prediction of(n,2n)reaction cross-sections of long-lived fission products based on tensor model

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Nuclear Science and Techniques 2024年第10期35卷 208-221页

作者： Jia-Li Huang Hui Wang Ying-Ge Huang Er-Xi Xiao Yu-Jie Feng Xin Lei Fu-Chang Gu Long Zhu Yong-Jing Chen Jun Su Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen UniversityZhuhai 519082China China Nuclear Data Center China Institute of Atomic EnergyBeijing 102413China Key Laboratory of Nuclear Data China Institute of Atomic EnergyBeijing 102413China

Interest has recently emerged in potential applications of(n,2n)reactions of unstable *** have arisen because of the scarcity of experimental cross-sectional *** study aims to predict the(n,2n)reaction cross-section of long-lived fission products based on a tensor *** tensor model is an extension of the collaborative filtering algorithm used for nuclear *** is based on tensor decomposition and completion to predict(n,2n)reaction cross-sections;the corresponding EXFOR data are applied as training *** reliability of the proposed tensor model was validated by comparing the calculations with data from EXFOR and different *** were made for long-lived fission products such as^(60)Co,^(79)Se,^(93)Zr,^(107)P,^(126)Sn,and^(137)Cs,which provide a predicted energy range to effectively transmute long-lived fission products into shorter-lived or less radioactive *** method could be a powerful tool for completing(n,2n)reaction cross-sectional data and shows the possibility of selective transmutation of nuclear waste.

关键词： (n,2n)Reaction cross-section Tensor model Machine learning collaborative filtering algorithm Selective transmutation

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The System of Personalized Learning Resource Recommendation and Experimental Teaching Based on collaborative filtering

The System of Personalized Learning Resource Recommendation ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Sun, Bin Tan, Hua Yang, Dongxiao Zhang, Yu Wu, Yefei Jin, Xinyu Zhejiang Univ Coll Informat Sci & Elect Hangzhou 310013 Peoples R China

ISBN: (纸本)9781665484855

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 *** 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 *** 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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An optimal approach for predicting cognitive performance in education based on deep learning

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COMPUTERS IN HUMAN BEHAVIOR 2025年 167卷

作者： Li, Deming Tang, Nie Chandler, Meredith Nanni, Emilio Jilin Int Studies Univ Sch Educ Changchun 130117 Jilin Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Dept Endocrinol Chengdu Peoples R China Southern Univ Baton Rouge LA 70813 USA MIT Cambridge MA 02139 USA

Research background: Knowledge tracking in educational data mining has become increasingly important for identifying students' knowledge gaps and enhancing individualized instruction. Traditional exercise recommendation algorithms often overlook students' forgetting behaviour, hindering effective learning. Purpose: This study aims to develop a novel approach to integrating the law of forgetting into a deep learningbased knowledge-tracking model, improving exercise recommendations and effectively addressing students' learning gaps. Methods: The proposed knowledge probability prediction model incorporates the forgetting curve theory alongside a dynamic key-value memory mechanism for tracking students' knowledge mastery levels. This model continuously adapts to students' interactions, allowing personalized exercise recommendations considering mastered and forgotten knowledge points. Results: The proposed model was evaluated using ASSISTment 2009, Statics 2011, and DouDouYun datasets. The results indicate that our approach significantly outperforms traditional recommendation algorithms regarding novelty and concept coverage, effectively accommodating students' forgetting behaviour. Conclusion: Integrating the forgetting law into knowledge-tracking systems leads to more effective and personalized exercise recommendations, ultimately facilitating improved learning outcomes. This approach enhances students' acquisition of new knowledge and more efficiently addresses their existing knowledge gaps.

关键词： Education Deep learning Knowledge tracking Data mining collaborative filtering algorithm

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