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Journal articles on the topic 'Intelligent recommendation system'

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Author: Grafiati

Published: 16 March 2023

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1

Kathait, ShailendraSingh, Shubhrita Tiwari, and PiyushKumar Singh. "INTELLIGENT RECOMMENDATION SYSTEM." International Journal of Advanced Research 5, no. 2 (February 28, 2017): 1649–56. http://dx.doi.org/10.21474/ijar01/3328.

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Mishra, Ikshita, Ankita Sharma, and Tanuj Deria. "Intelligent Tourist Recommendation System." IJARCCE 6, no. 4 (April 30, 2017): 384–91. http://dx.doi.org/10.17148/ijarcce.2017.6474.

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3

Rtili, Mohammed Kamal, Ali Dahmani, and Mohamed Khaldi. "Recommendation System Based on the Learners' Tracks in an Intelligent Tutoring System." Journal of Advances in Computer Networks 2, no. 1 (2014): 40–43. http://dx.doi.org/10.7763/jacn.2014.v2.79.

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4

Naik, Pratiksha Ashok. "Intelligent Food Recommendation System Using Machine Learning." Volume 5 - 2020, Issue 8 - August 5, no. 8 (August 27, 2020): 616–19. http://dx.doi.org/10.38124/ijisrt20aug414.

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The buying behavior of the consumer is affected by the suggestions given to the items. Recommendations can be made in the form of a review or ranking given to a specific product. Calories consumed by people contains carbohydrates, fats, proteins, minerals and vitamins, and any malnutrition causes severe health problems. In this paper, we propose a recommendation system which is trained on the basis of the recommendations received by the customer who has already used the product. Software recommends the product to the customer on the basis of the experience of the consumer using the same product. Each person has his or her own eating patterns, based on the preferences and dislikes of the user, indicating that personalized diet is important to sustain the success and health of the user. The proposed recommendation method uses a deep learning algorithm and a genetic algorithm to provide the best possible advice.

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Hirolikar, D. S., Ajinkya Satuse, Omkar Bhalerao, Pavan Pawar, and Hrithik Thorat. "Intelligent Movie Recommendation System Using AI and ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 611–22. http://dx.doi.org/10.22214/ijraset.2022.42255.

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Abstract: Recommender system are systems which provide you with a similar type of products or solutions and results, you are looking for. For example, if you go to a Clothing shop, you ask for a T-shirt with different designs or different colors, Then the shopkeeper recommends you with different colors. This recommending task for websites is done by recommending systems. A recommendation engine uses several algorithms to filter data and then recommends the most relevant items to consumers. A Movie Recommender system will recommend the most relevant and connected movie for the given category of search, if a user visits a movie site for the first time, the site will have no previous history of that user. In such cases, the user can search for their movie recommendations based on genre, year of release, director or actor and their favorite movie itself to get a new movie recommendation. Keywords: Movie Recommendation Systems, Content-Based Filtering, Movie recommendation, machine learning project

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6

Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (August 18, 2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

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7

., Jay Borade. "INTELLIGENT AGENT FOR TOURISM RECOMMENDATION SYSTEM." International Journal of Research in Engineering and Technology 07, no. 04 (April 25, 2018): 39–46. http://dx.doi.org/10.15623/ijret.2018.0704007.

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8

Cui, Xiaoyue. "An Adaptive Recommendation Algorithm of Intelligent Clothing Design Elements Based on Large Database." Mobile Information Systems 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/3334047.

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In the recent years, the developmental speed of intelligent technology continues to accelerate, and the research on the actual needs of users is also in depth. From the current situation of the clothing industry, how to combine artificial intelligence (AI) technology with clothing fashion has become the focus of customer’s attention. The application of intelligent clothing matching recommendation system (online) can effectively meet the needs of customers in dressing matching, so as to save a lot of time and energy (offline). With the maturity of artificial intelligence, machine learning, and other emerging computational technologies, the intelligent clothing matching system has laid a solid foundation. In this paper, several intelligent clothing matching recommendation systems that have been applied at present are deeply analyzed. Moreover, the basic algorithms and key technologies are elaborated in detail. In addition, the future research direction is found, so that the clothing matching recommendation system can be more personalized, and the comprehensive function is greatly improved in order to bring more ideal benefits.

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Mao, Qingqing, Aihua Dong, Qingying Miao, and Lu Pan. "Intelligent Costume Recommendation System Based on Expert System." Journal of Shanghai Jiaotong University (Science) 23, no. 2 (April 2018): 227–34. http://dx.doi.org/10.1007/s12204-018-1933-x.

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Chen, Qing Zhang, Yu Jie Pei, Yan Jin, and Li Yan Zhang. "Research on Intelligent Recommendation Method and its Application on Internet Bookstore." Advanced Materials Research 121-122 (June 2010): 447–52. http://dx.doi.org/10.4028/www.scientific.net/amr.121-122.447.

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As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

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Shao, Ruihua. "Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System." Advances in Multimedia 2022 (July 14, 2022): 1–13. http://dx.doi.org/10.1155/2022/7860718.

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In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

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12

Poorani, G., S. Mohammed Rishwan, R. Pavan Kumar, and M. Ragul. "Sustainable Intelligent Prediction and Price Recommendation System." Journal of Physics: Conference Series 1916, no. 1 (May 1, 2021): 012086. http://dx.doi.org/10.1088/1742-6596/1916/1/012086.

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Lin, Qi. "Intelligent Recommendation System Based on Image Processing." Journal of Physics: Conference Series 1449 (January 2020): 012131. http://dx.doi.org/10.1088/1742-6596/1449/1/012131.

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Ben Zaken, Daniel, Kobi Gal, Guy Shani, Avi Segal, and Darlene Cavalier. "Intelligent Recommendations for Citizen Science." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 14693–701. http://dx.doi.org/10.1609/aaai.v35i17.17726.

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Citizen science refers to scientific research that is carried out by volunteers, often in collaboration with professional scientists. The spread of the internet has allowed volunteers to contribute to citizen science projects in dramatically new ways while creating scientific value and gaining pedagogical and social benefits. Given the sheer size of available projects, finding the right project, which best suits the user preferences and capabilities, has become a major challenge and is essential for keeping volunteers motivated and active contributors. We address this challenge by developing a system for personalizing project recommendations which was fully deployed in the wild. We adapted several recommendation algorithms to the citizen science domain from the literature based on memory-based and model-based collaborative filtering approaches. The algorithms were trained on historical data of users' interactions in the SciStarter platform - a leading citizen science site -as well as their contributions to different projects. The trained algorithms were evaluated in SciStarter and involved hundreds of users who were provided with personalized recommendations for new projects they had not contributed to before. The results show that using the new recommendation system led people to increased participation in new SciStarter projects when compared to groups that were recommended projects using non-personalized recommendation approaches, and compared to behavior before recommendations. In particular, the group of volunteers receiving recommendations created by an SVD algorithm (matrix factorization) exhibited the highest levels of contributions to new projects, when compared to the other cohorts. A follow-up survey conducted with the SciStarter community confirmed that users felt that the recommendations matched their personal interests and goals. Based on these results, our recommendation system is now fully integrated into the SciStarter portal, positively affecting hundreds of users each week, and leading to social and educational benefits.

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Pooja and Vishal Bhatnagar. "A Prospect on an Intelligent Recommender System." International Journal of Service Science, Management, Engineering, and Technology 12, no. 2 (March 2021): 25–43. http://dx.doi.org/10.4018/ijssmet.2021030102.

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User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies the areas of the future for the development of an intelligent system.

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Rani, Geeta, Vijaypal Singh Dhaka, Sonam, Upasana Pandey, and Pradeep Kumar Tiwari. "Intelligent and Adaptive Web Page Recommender System." International Journal of Web Services Research 18, no. 4 (October 2021): 27–50. http://dx.doi.org/10.4018/ijwsr.2021100102.

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In this manuscript, an intelligent and adaptive web page recommender system is proposed that provides personalized, global, and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: uniformity and recommendation strength. The system continuously tracks the user's responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset, which is a significant improvement over the 70% F1 measure reported by automatic clustering-based genetic algorithm, the prior web recommender system.

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17

Jiang, Ji, and Jian Gang Tang. "Research on Intelligent Knowledge Recommendation System for Police Applications." Applied Mechanics and Materials 530-531 (February 2014): 447–51. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.447.

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This paper proposed knowledge content tag recommendation algorithm in cloud computing Environment, and applied to police information knowledge. The algorithm analyzed user behavior history of operation and considered the similarity knowledge of the entries on the tag of police information, marked weight of tag in predicting when a user rating. On this basis, the police information implementations specific recommendations based on the specific user application knowledge. Meanwhile, combined the tag of system entry contents correlation with user correlation analysis, and solved the problems of system sparse matrix. Finally, the results demonstrated the effectiveness and superiority of recommendation algorithm.

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18

Li, Pengjiao, and Jun Yang. "PSO Algorithm-Based Design of Intelligent Education Personalization System." Computational Intelligence and Neuroscience 2022 (July 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/9617048.

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The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.

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Kim, Gui-Jung, Bong-Han Kim, and Jung-Soo Han. "Customizing Intelligent Recommendation System based on Compound Knowledge." Journal of the Korea Contents Association 10, no. 8 (August 28, 2010): 26–31. http://dx.doi.org/10.5392/jkca.2010.10.8.026.

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20

Roshan, Shiva Hassanjani, Seyyed Javad Kazemitabar, and Ghorban Kheradmandian. "Intelligent Chemical Fertilizer Recommendation System for Rice Fields." Asian Journal of Applied Science and Technology 05, no. 03 (2021): 184–95. http://dx.doi.org/10.38177/ajast.2021.5318.

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21

Zheng, Fangxia. "Personalized Education Based on Hybrid Intelligent Recommendation System." Journal of Mathematics 2022 (January 17, 2022): 1–9. http://dx.doi.org/10.1155/2022/1313711.

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Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.

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Ou, Tsung-Yin, Guan-Yu Lin, Hsin-Pin Fu, Shih-Chia Wei, and Wen-Lung Tsai. "An Intelligent Recommendation System for Real Estate Commodity." Computer Systems Science and Engineering 42, no. 3 (2022): 881–97. http://dx.doi.org/10.32604/csse.2022.022637.

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23

Trappey, Amy J. C., Charles V. Trappey, Chun-Yi Wu, Chin Yuan Fan, and Yi-Liang Lin. "Intelligent patent recommendation system for innovative design collaboration." Journal of Network and Computer Applications 36, no. 6 (November 2013): 1441–50. http://dx.doi.org/10.1016/j.jnca.2013.02.035.

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Chang, Jui-Hung, Chin-Feng Lai, Ming-Shi Wang, and Tin-Yu Wu. "A cloud-based intelligent TV program recommendation system." Computers & Electrical Engineering 39, no. 7 (October 2013): 2379–99. http://dx.doi.org/10.1016/j.compeleceng.2013.04.025.

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Li, Hui, Haining Li, Shu Zhang, Zhaoman Zhong, and Jiang Cheng. "Intelligent learning system based on personalized recommendation technology." Neural Computing and Applications 31, no. 9 (June 6, 2018): 4455–62. http://dx.doi.org/10.1007/s00521-018-3510-5.

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Xin, Xin, Tianlei Shi, and Mishal Sohail. "Knowledge-Based Intelligent Education Recommendation System with IoT Networks." Security and Communication Networks 2022 (March 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/4140774.

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The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this paper conducts in-depth mining of the potential attributes of users and items and improves the matrix decomposition formula based on knowledge recommendation requirements. In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements. Finally, this paper designs experiments to verify and analyze the model from the perspective of model performance and user satisfaction. The research results show that the algorithm model constructed in this paper is effective.

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27

Gao, Chenchen, and Kai Zhang. "Research on remote education intelligent recommendation system of computer network Ancient Literature Resources Database." MATEC Web of Conferences 365 (2022): 01055. http://dx.doi.org/10.1051/matecconf/202236501055.

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With the development of intelligent education and the development of distance education becoming more and more intelligent and three-dimensional, an intelligent recommendation system is constructed on the basis of learners'individual demands, will achieve learning requirements and recommended content of the precise match. The construction of ancient literature resource database by means of computer network and the timely push of intelligent recommendation system to students will promote the essential change of distance education on the basis of reconstructing the ecology of distance education. This paper analyzes the significance of the remote education intelligent recommendation system of the computer network ancient literature resources database, and probes into the construction strategy of the remote education intelligent recommendation system of the computer network ancient literature resources database.

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Li, Liuqing. "Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (February 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/6602471.

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In order to improve the effect of cross-border e-commerce intelligent information recommendation, this paper applies deep learning to the intelligent information processing and intelligent recommendation of e-commerce and proposes an improved version of the topic model to solve the problem of feature extraction of the text of the recommendation system. In order to deal with translation problems, this paper proposes an end-to-end sequence-to-sequence learning method. In addition, this study uses the long tail theory to excavate the mass commodities in the niche and recommends these products to users as suggestions. Finally, this paper proposes a niche product recommendation algorithm based on the graph search strategy based on the graph model. The experiment shows that the cross-border e-commerce intelligent information recommendation system based on deep learning proposed in this paper has a good recommendation effect and meets the recommendation needs of cross-border e-commerce.

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Wang, Pu. "A Collaborative Filtering Recommendation Algorithm Based on Product Clustering." Applied Mechanics and Materials 267 (December 2012): 87–90. http://dx.doi.org/10.4028/www.scientific.net/amm.267.87.

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E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

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Xu, Qin, and Jun Wang. "A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System." Computational Intelligence and Neuroscience 2022 (March 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/9501246.

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E-commerce product recommendation system can help users to find their own products quickly from a large number of products. To address the shortcomings of the current e-commerce product recommendation system, such as low efficiency and large recommendation errors, we designed an intelligent recommendation system based on social awareness and mobile computing. The behavioral characteristics of the current e-commerce product recommendation system are analyzed; the e-commerce product recommendation system is built according to the data processing technology of mobile computing, and the key technologies of the e-commerce product recommendation system are designed. The test results show that the proposed system overcomes the shortcomings of the traditional e-commerce product recommendation system, speeds up the speed of users to find the products they really need from a large number of products, improves the accuracy of e-commerce product recommendations, and the error of e-commerce product recommendations is much lower than that of the traditional, which has higher practical application value.

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Xu, Shasha, and Xiufang Yin. "Recommendation System for Privacy-Preserving Education Technologies." Computational Intelligence and Neuroscience 2022 (April 16, 2022): 1–8. http://dx.doi.org/10.1155/2022/3502992.

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Considering the priority for personalized and fully customized learning systems, the innovative computational intelligent systems for personalized educational technologies are the timeliest research area. Since the machine learning models reflect the data over which they were trained, data that have privacy and other sensitivities associated with the education abilities of learners, which can be vulnerable. This work proposes a recommendation system for privacy-preserving education technologies that uses machine learning and differential privacy to overcome this issue. Specifically, each student is automatically classified on their skills in a category using a directed acyclic graph method. In the next step, the model uses differential privacy which is the technology that enables a facility for the purpose of obtaining useful information from databases containing individuals’ personal information without divulging sensitive identification about each individual. In addition, an intelligent recommendation mechanism based on collaborative filtering offers personalized real-time data for the users’ privacy.

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Abdul Kareem, Emad I., and Haider K. Hoomod. "Integrated tripartite modules for intelligent traffic light system." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (June 1, 2022): 2971. http://dx.doi.org/10.11591/ijece.v12i3.pp2971-2985.

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<span>The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.</span>

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33

Dai, Hong Qin. "The Research on Intelligent Clothing Recommendation System Based on Ontology." Advanced Materials Research 175-176 (January 2011): 827–31. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.827.

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Ontology which is a description of knowledge has been applied to many fields. Some intelligent systems based on ontology have been developed. In the paper, a clothing recommendation system based on ontology is developed. The recommendation system mainly includes two parts: knowledge base and inference engine. The structural knowledge of clothing is represented by using ontology and some constraint knowledge is described by SWRL. The clothing recommendation process are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based clothing knowledge and SWRL-based design rules into JESS facts and JESS rules, respectively.

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Li, Xing Yuan, and Qing Shui Li. "An Improved Personalized Recommendation System Research." Advanced Materials Research 756-759 (September 2013): 1398–402. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1398.

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In order to find information of interest and found valuable information resources in enrich Internet data. This paper describes a personalized recommendation system, personalized recommendation system is an intelligent recommendation system to help e-commerce site for customers to provide complete personalized shopping decision support and information services. for the User Rating data extreme sparseness, This paper presents nearest neighbor collaborative filtering algorithm based on project score predicted ,experiments show that this method can improve the quality of recommendation system.

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35

Bi, Shengqin. "Intelligent system for English translation using automated knowledge base." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5057–66. http://dx.doi.org/10.3233/jifs-179991.

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In the process of globalization, machine translation has undergone a long period of evolution and development. Although the development level of machine translation has been greatly improved, the quality of machine translation is still not very high, and it is difficult to meet the needs of users. Artificial intelligence is the science that studies the laws of human intelligent activity. The application of artificial intelligence technology in the English depression and depression, combined with the Internet and intelligent knowledge base, can develop English translation systems to solve the problem of English translation to a certain extent. Based on the above background, the research content of this article is a neural network-based artificial intelligence technology English translation system based on the intelligent knowledge base. This article is mainly based on the existing English-Chinese machine translation to find a more favorable method for English long sentence translation. By improving part-of-speech tagging and rules, the rules can match more sentence patterns to improve the quality of existing machine translations. This paper proposes an improved hybrid recommendation algorithm, and through experimental simulation, the results show that the accuracy of the algorithm is not very high. The highest is 35.64%. The possible reason may be that the k value is selected during k-means text clustering, or the N value recommended by TopN is not selected properly, but the hybrid recommendation is still better than ordinary collaborative filtering.

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36

Xiong, Wenzuixiong, and Yichao Zhang. "An intelligent film recommender system based on emotional analysis." PeerJ Computer Science 9 (March 9, 2023): e1243. http://dx.doi.org/10.7717/peerj-cs.1243.

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The existing personalized film recommendation methods take the user’s historical rating as an important basis for recommendation. However, the user’s rating standards are different, so it is difficult to mine the user’s real preferences and form accurate push. Therefore, to achieve high-quality personalized recommendation of films, it is particularly important to mine the emotion of user reviews. In this article, a personalized recommendation method based on sentiment analysis of film reviews is proposed, where natural language processing technology is used to mine the emotional tendency of user reviews. The multi-modal emotional features are weighted and the weighted fusion feature vector after PSO is taken as the overall emotion vector, then the emotional similarity of weighted fusion is calculated by considering the time factor of content publishing and the average emotional tendency of users. By calculating the matching degree of emotional value between users and films, the top-N film recommendation for target users is given. The test results show that the effect of the personalized film recommendation system based on multimodality is superior to that of the comparison method, which effectively solves the problem of different user rating scales, and really increases users’ interest in watching films.

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Peng, Bo. "Research and Implementation of Electronic Commerce Intelligent Recommendation System Based on the Fuzzy Rough Set and Improved Cellular Algorithm." Mathematical Problems in Engineering 2021 (January 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/6671219.

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With the continuous development of e-commerce, our society has transitioned from a mechanical era to an intelligent era. There have been many things that have subverted people’s traditional concepts, and they have also completely changed the way of life of modern people. Due to the development of e-commerce, people can enjoy the scenery and food from all over the world at home. Online shopping and online ticket purchase have greatly facilitated people’s lives and given people more choices. However, due to the excessive selection of things, there is also a phenomenon of information overload. Sometimes, it is difficult for people to find a product or content that they are very satisfied with. So, how to analyze people’s browsing behavior and predict what kind of content people want and how to push products on major websites have become a major issue facing major online companies. Based on this, this paper proposes an e-commerce intelligent recommendation technology based on the fuzzy rough set and improved cellular algorithm. It provides personalized recommendations for users based on their browsing history and purchase history. The research of this article is mainly divided into four parts. The first part is to analyze the status quo of technical research in this field. By analyzing the shortcomings of the existing technology, the concept of this article is proposed. The second part introduces the classic intelligent recommendation algorithm, including the principle and process of the fuzzy rough set and improved honeycomb algorithm, and analyzes the difference of various recommendation algorithms to illustrate the adaptability of each algorithm in practical applications and their respective advantages and disadvantages. The third part introduces an intelligent recommendation system based on fuzzy clustering, comprehensively analyzes the characteristics of users and commodities, makes full use of users’ evaluation information of commodities, and realizes intelligent recommendation based on content and collaborative filtering. At the end of the article, through comparative analysis experiments, the superiority of the intelligent recommendation system for electronic commerce based on the fuzzy rough set and improved cellular algorithm is further proved, and the accuracy of intelligent recommendation is improved.

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Borovyk, Maryna. "IMPROVING THE QUALITY OF MANAGEMENT DECISIONS MAKING AT THE ACCOUNT OF USING THE INTELLECTUAL RECOMMENDATION SYSTEM." Scientific Notes of Ostroh Academy National University, "Economics" Series 1, no. 18(46) (September 24, 2020): 26–30. http://dx.doi.org/10.25264/2311-5149-2020-18(46)-26-30.

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The article considers the essence and content of the process of making managerial decisions in the activities of higher education institutions. It is determined that a quality management decision in the context of higher education institutions is the result of analysis, forecasting, economic justification and choice of alternatives from a variety of options aimed at achieving sustainable development of higher education institutions. The essence of the intelligent recommendation system of management decision support is described, the use of which allows managers to combine their own subjective advantages with machine analysis of the situation in making recommendations in the management decision-making process aimed at achieving certain goals of higher education. The advantages of an intelligent recommendation system in comparison with traditional automated management decision support systems are determined. The main functions of the intelligent recommendation system to support management decisions are considered, including: situation analysis; generation of possible management decisions to solve the tasks; evaluation of generated scenarios and selection of the best; ensuring constant exchange of information on the status of the decision and coordination of group decisions; modeling of decisions; computer analysis of possible consequences of management decisions; collection of data on the results of implementation of management decisions and evaluation of their results. The main ways to improve the quality of management decisions in the activities of higher education institutions to ensure their sustainable development are identified. The expediency of using an intelligent recommendation system of decision support to improve the quality of management decisions in the activities of higher education institutions in order to achieve sustainable development is substantiated. It is proposed to combine the knowledge and experience of managers with automated support of this process to improve the quality of management decisions in the activities of higher education institutions and to achieve sustainable development, which will increase the validity of management decisions through the use of intelligent recommendation system to support management decisions.

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Lin, Hanhui, Shaoqun Xie, Zhiguo Xiao, Xinxin Deng, Hongwei Yue, and Ken Cai. "Adaptive Recommender System for an Intelligent Classroom Teaching Model." International Journal of Emerging Technologies in Learning (iJET) 14, no. 05 (March 14, 2019): 51. http://dx.doi.org/10.3991/ijet.v14i05.10251.

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The development of information technology has facilitated the use of the intelligent classroom model supported by information technology to improve the college students’ comprehensive quality and ability. However, the existing models are too sophisticated to be applied to the actual teaching process, and ignore the individualized teaching characteristics of students. Therefore, an intelligent classroom model with adaptive learning resource recommendation was proposed. First, the entire teaching process was divided into three stages which were used to combine teachers’ teaching and students’ learning. Then the key problems of the learning resources recommendation system was studied and a learning resource recommendation based on TR-LDA (Teaching Resources-Latent Dirichlet Allocation) was proposed and how to be achieved. Finally, the proposed intelligent classroom model was verified in practical teaching. Results show that the intelligent classroom model with adaptive learning resources recommendation can help to improve students’ learning efficiency. The relevant conclusions can be used as a reference for exploring the use of information technology to improve the quality of undergraduate professional course teaching.

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Jiang, Weijin, Jiahui Chen, Yirong Jiang, Yuhui Xu, Yang Wang, Lina Tan, and Guo Liang. "A New Time-Aware Collaborative Filtering Intelligent Recommendation System." Computers, Materials & Continua 61, no. 2 (2019): 849–59. http://dx.doi.org/10.32604/cmc.2019.05932.

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Zhi-hang, Tang, Guo Tao, Li Jun, and Wu Shi-qi. "Intelligent Recommendation System Based On K-Means Clustering Algorithm." International Journal of Advanced Networking and Applications 11, no. 05 (2020): 4393–98. http://dx.doi.org/10.35444/ijana.2020.11053.

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Ghnemat, Rawan, and Edward Jaser. "Toward Mobile Telecommunication Recommendation System through Intelligent Customers Categorization." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 7 (February 15, 2014): 3651–58. http://dx.doi.org/10.24297/ijct.v12i7.3099.

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Now a day, usage of mobile devices is becoming indispensable. This is evident with current mobile penetration rates reaching 100% and even more in some countries. Customers across the world are enjoying competitive prices due to high competition among telecommunication companies. As a result of this, it is mandatory for mobile companies to provide high quality services to their customers to retain them. One aspect which will maximize customersâ€™ trust and lead to high retention rate is to offer them a suitable plan that matches their usage.Â Mobile customer usage categorization is therefore an essential task to develop intelligent business plans. Personalized recommendation system is needed to dynamically adapt the different customer behaviours with the most appropriate plan for them. In this paper we propose a new automatic approach for costumersâ€™ categorization. This will be the basis for the recommendation system. The proposed method is built using Fuzzy rule and aims at usage behaviour prediction. The rules was extracted from real customer data obtained from a leading provider. Comparison study with other categorization methods has been conducted and showed superior result and demonstrated the potential advantage of the proposed fuzzy based method.

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Boutas, Sotirios D., Ioannis E. Anagnostopoulos, Vassili Loumos, and Eleftherios Kayafas. "An intelligent web recommendation system for ubiquitous geolocation awareness." International Journal of Ad Hoc and Ubiquitous Computing 14, no. 1 (2013): 1. http://dx.doi.org/10.1504/ijahuc.2013.056270.

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Subramaniyaswamy, V., R. Logesh, and V. Indragandhi. "Intelligent sports commentary recommendation system for individual cricket players." International Journal of Advanced Intelligence Paradigms 10, no. 1/2 (2018): 103. http://dx.doi.org/10.1504/ijaip.2018.089492.

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Logesh, R., V. Indragandhi, and V. Subramaniyaswamy. "Intelligent sports commentary recommendation system for individual cricket players." International Journal of Advanced Intelligence Paradigms 10, no. 1/2 (2018): 103. http://dx.doi.org/10.1504/ijaip.2018.10010529.

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Zhang, Junjie, Kaixuan Liu, Min Dong, Hua Yuan, Chun Zhu, and Xianyi Zeng. "An intelligent garment recommendation system based on fuzzy techniques." Journal of The Textile Institute 111, no. 9 (December 3, 2019): 1324–30. http://dx.doi.org/10.1080/00405000.2019.1694351.

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卢, 梦丽. "Intelligent Gift Recommendation System Based on User Personality Analysis." Computer Science and Application 10, no. 05 (2020): 978–89. http://dx.doi.org/10.12677/csa.2020.105101.

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Lin, Jinjiao, Haitao Pu, Yibin Li, and Jian Lian. "Intelligent Recommendation System for Course Selection in Smart Education." Procedia Computer Science 129 (2018): 449–53. http://dx.doi.org/10.1016/j.procs.2018.03.023.

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Jaiswal, Saurabh, Shubham Virmani, Vishal Sethi, Kanjar De, and Partha Pratim Roy. "An intelligent recommendation system using gaze and emotion detection." Multimedia Tools and Applications 78, no. 11 (November 3, 2018): 14231–50. http://dx.doi.org/10.1007/s11042-018-6755-1.

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Wenwen, Zhou. "Building an Urban Smart Community System Based on Association Rule Algorithms." Security and Communication Networks 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/8773259.

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Intelligent system development is an integral component of smart community development and has a significant impact on the development of smart communities. Some cities continue to implement personalized smart community services, resulting in the formation of smart city communities with unique characteristics. Urban smart communities are based on the principle of owner-occupant convenience, integrating a wealth of community information and making it more relevant to each and every resident through intelligent management. Increasing information transmission rates have enhanced the ability of smart community systems to integrate information, but the smart community recommendation method is still based on traditional categorized recommendations. This paper addresses the deficiency of recommended information in smart urban communities. By analyzing user interaction and operation data, we can determine the interest and recognition of browsing attractions among users. Compared to conventional classification recommendations, weighted association rules can identify potentially very important rules applicable to small groups, thereby meeting the needs of various groups and enabling personalized services. Through continuous feedback from user behavior data, the system gradually identifies the community information that users are interested in during the specific recommendation process. After testing, the smart community system’s recommendation accuracy and real-time performance have vastly improved in comparison to categorical recommendations, and it can effectively meet the needs of tenants for community recommendations.

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