Academic literature on the topic 'COMmunity interest based RECommendation system'
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Journal articles on the topic "COMmunity interest based RECommendation system"
Zhang, Hong, Dechu Ge, and Siyu Zhang. "Hybrid recommendation system based on semantic interest community and trusted neighbors." Multimedia Tools and Applications 77, no. 4 (March 20, 2017): 4187–202. http://dx.doi.org/10.1007/s11042-017-4553-9.
Full textZheng, Jianxing, Suge Wang, Deyu Li, and Bofeng Zhang. "Personalized recommendation based on hierarchical interest overlapping community." Information Sciences 479 (April 2019): 55–75. http://dx.doi.org/10.1016/j.ins.2018.11.054.
Full textZheng, Jianxing, and Yanjie Wang. "Personalized Recommendations Based on Sentimental Interest Community Detection." Scientific Programming 2018 (August 5, 2018): 1–14. http://dx.doi.org/10.1155/2018/8503452.
Full textWenwen, 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.
Full textZhou, Tom, Hao Ma, Michael Lyu, and Irwin King. "UserRec: A User Recommendation Framework in Social Tagging Systems." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 5, 2010): 1486–91. http://dx.doi.org/10.1609/aaai.v24i1.7524.
Full textGan, Mingxin, and Xiongtao Zhang. "Integrating Community Interest and Neighbor Semantic for Microblog Recommendation." International Journal of Web Services Research 18, no. 2 (April 2021): 54–75. http://dx.doi.org/10.4018/ijwsr.2021040104.
Full textTang, Lei, Dandan Cai, Zongtao Duan, Junchi Ma, Meng Han, and Hanbo Wang. "Discovering Travel Community for POI Recommendation on Location-Based Social Networks." Complexity 2019 (February 12, 2019): 1–8. http://dx.doi.org/10.1155/2019/8503962.
Full textShokrzadeh, Zeinab, Mohammad-Reza Feizi-Derakhshi, Mohammad-Ali Balafar, and Jamshid Bagherzadeh Mohasefi. "Graph-Based Recommendation System Enhanced by Community Detection." Scientific Programming 2023 (August 21, 2023): 1–12. http://dx.doi.org/10.1155/2023/5073769.
Full textKumar, Akshi, and Saurabh Raj Sangwan. "Expert Finding in Community Question-Answering for Post Recommendation." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.4.16764.
Full textLiu, Jing, and Yong Zhong. "Time-Weighted Community Search Based on Interest." Applied Sciences 12, no. 14 (July 13, 2022): 7077. http://dx.doi.org/10.3390/app12147077.
Full textDissertations / Theses on the topic "COMmunity interest based RECommendation system"
Khater, Shaymaa. "Personalized Recommendation for Online Social Networks Information: Personal Preferences and Location Based Community Trends." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/64283.
Full textPh. D.
JAIN, ABHA. "INTEREST MINING FOR RECOMMENDATION SYSTEM IN VIRTUAL COMMUNITIES." Thesis, 2015. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14297.
Full textDr. AKSHI KUMAR Assistant Professor DEPARTMENT OF SOFTWARE ENGINEERING DELHI TECHNOLOGICAL UNIVERSITY 2011
Chen, I.-Ru, and 陳怡如. "A Study on the Recommendation System Based on Interest Map." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/32724019438553890272.
Full text國立交通大學
經營管理研究所
95
By applying the concept of social network into recommendation system, we convert the relationships between interests into ‘Interest Map’, just as the social network looks like. According to the association strength, the system could recommend users interests from interests. The goal of study is to verify if the recommendation system based on Interest Map is feasible, and to compare the relative advantages of immediate computation, and dynamic system over the general recommendation systems. The relationship between two interests, here we call it association, is built when someone likes these two interests at the same time. Repeating the process of association-building, we make Interest Map. After recommendation, which is selected from the strongest strength of associations, we compute the precision rate and recall rate to verify if the recommendation system based on Interest Map is feadible. Our study suggests that the feature of immediate computation is achieved by the dynamic algorithm, and meets the need of routine update of the general recommendation systems. By this process, users could get the newest recommendation at any time, and may enhance the recommendation and user trust. Besides, dynamic system improves the efficiency of recommendation system. The feature of dynamic system allows the recommendation system to check the Interest Map inside and update in time, and makes the recommendation system at a prepared condition to response users’ request. Owing to the reasons above, the recommendation system based on Interest Map is feasible and has some relative advantages over the general recommendation systems.
Wu, Chien-Liang, and 吳建良. "A Web Page Recommendation System Based on Clusters of Query Interest." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/90203635016331588291.
Full text國立臺灣師範大學
資訊教育研究所
90
Most previous works on recommendation systems of web pages were designed based on collaborative filtering according to the clusters of user browsing behavior. In these approaches, a user only belongs to certain one cluster. If most users have multiple kinds of browsing interests, the number of users in the same cluster will be small and the information used for recommendation is limited. In addition, the information of users who have partially similar behavior is not considered. In this thesis, the strategies for constructing a query and recommendation system of web pages are proposed. First, the query keywords, browsed web pages, and user feedback values are extracted from web logs to be query transactions. A clustering algorithm is proposed to find the clusters of queries and related web pages, called the clusters of query interest , from the query transactions. A user who has multiple kinds of query interests can belong to more than one cluster. Then user query transactions are partitioned based on the clusters of query interest. In each partition, the association rules of queries and web pages are mined, where the support and confidence of rules are computed based on feedback values of users. According to the mined information, two main functions are provided in the system. A member user can ask a recommendation request. Based on clusters of query interest contained in the user profile, the highly associated web pages are recommended. On the other hand, an anonymous user can ask a query recommendation request to the system by giving query keywords. According to the cluster of query interest that the query keywords belong to, the highly associated web pages are returned as query results. Therefore, the query results will be more simplified and meet the requirements of most users.
Yu, Wei Ting, and 魏廷宇. "The Study of Virtual Community Peer Recommendation System Based on Social Relationship." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/44751520102638124880.
Full text輔仁大學
資訊管理學系
93
Knowledge has become the most important production element in the era of knowledge economy. Knowledge contains two parts - explicit knowledge and implicit one. If and only if we understand the two parts of knowledge, we say we understand knowledge. As the progress of information technology, virtual community in the Internet becomes the main platform to share knowledge. However, because of the characters of the post in the virtual community, the contented-based recommendation system does not fit. Moreover, collaborative recommendation system gets the problem called “ratings sparsity”. In the other way, the current recommendation systems do not consider the social relationship which is an important issue when people share knowledge. This thesis implemented 6 recommendation modules based on 6 measures which are used to estimate the social relationships between two members in a forum – a kind of virtual community in the Internet. When some member A creates a new topic, the recommendation modules will recommend people who are willing to discuss with A. This thesis used the data of a virtual community to understand the forecasting ability of the 6 recommendation modules based on social relationships. The experiment result shows that the greatest forecasting ability of recommendation module is based on the “mostpost” social relationship measure. In addition, computing relationship in the light of some specific members, not all of members, can increase the forecasting ability of recommendation modules, no matter based on what kind of measures.
Books on the topic "COMmunity interest based RECommendation system"
Tietje, Christian, and Andrej Lang. Community Interests in World Trade Law. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198825210.003.0012.
Full textGalera, Giulia. Social and Solidarity Co-operatives. Edited by Jonathan Michie, Joseph R. Blasi, and Carlo Borzaga. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199684977.013.12.
Full textGlazov, M. M. Electron & Nuclear Spin Dynamics in Semiconductor Nanostructures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198807308.001.0001.
Full textWikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Full textBook chapters on the topic "COMmunity interest based RECommendation system"
He, Jianming, and Wesley W. Chu. "Design Considerations for a Social Network-Based Recommendation System (SNRS)." In Community-Built Databases, 73–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19047-6_4.
Full textGurini, Davide Feltoni, Fabio Gasparetti, Alessandro Micarelli, and Giuseppe Sansonetti. "iSCUR: Interest and Sentiment-Based Community Detection for User Recommendation on Twitter." In User Modeling, Adaptation, and Personalization, 314–19. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08786-3_27.
Full textInterdonato, Roberto, and Andrea Tagarelli. "Personalized Recommendation of Points-of-Interest Based on Multilayer Local Community Detection." In Lecture Notes in Computer Science, 552–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67217-5_33.
Full textWang, Yuehua, Zhinong Zhong, Anran Yang, and Ning Jing. "A Deep Point-of-Interest Recommendation System in Location-Based Social Networks." In Data Mining and Big Data, 547–54. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93803-5_51.
Full textRoy, Sohom, Sayan Kundu, Dhrubasish Sarkar, Chandan Giri, and Premananda Jana. "Community Detection and Design of Recommendation System Based on Criminal Incidents." In Advances in Intelligent Systems and Computing, 71–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7834-2_7.
Full textSantos, Filipe, Ana Almeida, Constantino Martins, Paulo Oliveira, and Ramiro Gonçalves. "Tourism Recommendation System based in User Functionality and Points-of-Interest Accessibility levels." In Advances in Intelligent Systems and Computing, 275–84. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48523-2_26.
Full textRavi, Logesh, V. Subramaniyaswamy, V. Vijayakumar, Rutvij H. Jhaveri, and Jigarkumar Shah. "Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation." In Advances in Intelligent Systems and Computing, 311–21. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9953-8_27.
Full textUgli, Sadriddinov Ilkhomjon Rovshan, Doo-Soon Park, Daeyoung Kim, Yixuan Yang, Sony Peng, and Sophort Siet. "Movie Recommendation System Using Community Detection Based on the Girvan–Newman Algorithm." In Advances in Computer Science and Ubiquitous Computing, 599–605. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1252-0_80.
Full textTang, Tiffany, and Gordon McCalla. "Beyond Learners’ Interest: Personalized Paper Recommendation Based on Their Pedagogical Features for an e-Learning System." In PRICAI 2004: Trends in Artificial Intelligence, 301–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_33.
Full textMassimo, David, and Francesco Ricci. "Next-POI Recommendations Matching User’s Visit Behaviour." In Information and Communication Technologies in Tourism 2021, 45–57. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_4.
Full textConference papers on the topic "COMmunity interest based RECommendation system"
"MULTI-INTEREST COMMUNITIES AND COMMUNITY-BASED RECOMMENDATION." In 3rd International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001273800370045.
Full textAhmed, Kazi Wasif, Md Mamunur Rashid, Md Kamrul Hasan, and Hasan Mahmud. "Cohesion based personalized community recommendation system." In 2015 18th International Conference on Computer and Information Technology (ICCIT). IEEE, 2015. http://dx.doi.org/10.1109/iccitechn.2015.7488038.
Full textNandagawali, Priyanka A., and Jaikumar M. Patil. "Community based recommendation system based on products." In 2014 International Conference on Power Automation and Communication (INPAC). IEEE, 2014. http://dx.doi.org/10.1109/inpac.2014.6981153.
Full textJain, Shainee, Tejaswi Pawar, Heth Shah, Omkar Morye, and Bhushan Patil. "Video Recommendation System Based on Human Interest." In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT). IEEE, 2019. http://dx.doi.org/10.1109/iciict1.2019.8741428.
Full textYu, Yunfei, and Yinghua Zhou. "Research on recommendation system based on interest clustering." In 11TH ASIAN CONFERENCE ON CHEMICAL SENSORS: (ACCS2015). Author(s), 2017. http://dx.doi.org/10.1063/1.4977377.
Full textLi, Chong, Kunyang Jia, Dan Shen, C. J. Richard Shi, and Hongxia Yang. "Hierarchical Representation Learning for Bipartite Graphs." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/398.
Full textByun, Sung-Woo, So-Min Lee, Seok-Pil Lee, Kwang-Yong Kim, and Cho Kee-Seong. "A recommendation system based on object of the interest." In 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423521.
Full textByun, Sung-Woo, So-Min Lee, Seok-Pil Lee, Kwang-Yong Kim, and Kee-Seong Cho. "A recommendation system based on object of the interest." In 2016 18th International Conference on Advanced Communication Technology (ICACT). IEEE, 2016. http://dx.doi.org/10.1109/icact.2016.7423522.
Full textYin, Bin, Yujiu Yang, and Wenhuang Liu. "ICSRec: Interest circle-based recommendation system incorporating social propagation." In 2014 4th IEEE International Conference on Information Science and Technology (ICIST). IEEE, 2014. http://dx.doi.org/10.1109/icist.2014.6920377.
Full textZhou, Xuan, Xiaoming Wang, Guangyao Pang, Yaguang Lin, Pengfei Wan, and Meiling Ge. "Dual Attention-based Interest Network for Personalized Recommendation System." In 2021 IEEE 15th International Conference on Big Data Science and Engineering (BigDataSE). IEEE, 2021. http://dx.doi.org/10.1109/bigdatase53435.2021.00010.
Full textReports on the topic "COMmunity interest based RECommendation system"
Yuebin, Xu. Development and Performance of the Elderly Care System in the People’s Republic of China. Asian Development Bank, August 2021. http://dx.doi.org/10.22617/wps210303-2.
Full textFord, Adam T., Marcel Huijser, and Anthony P. Clevenger. Long-term responses of an ecological community to highway mitigation measures. Nevada Department of Transportation, June 2022. http://dx.doi.org/10.15788/ndot2022.06.
Full textAharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
Full textRosen, Michael, C. Matthew Stewart, Hadi Kharrazi, Ritu Sharma, Montrell Vass, Allen Zhang, and Eric B. Bass. Potential Harms Resulting From Patient-Clinician Real-Time Clinical Encounters Using Video-based Telehealth: A Rapid Evidence Review. Agency for Healthcare Research and Quality (AHRQ), September 2023. http://dx.doi.org/10.23970/ahrqepc_mhs4telehealth.
Full textDoo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, August 2021. http://dx.doi.org/10.4271/epr2021017.
Full textBurns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
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