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Relevant bibliographies by topics / COMmunity interest based RECommendation system

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Academic literature on the topic 'COMmunity interest based RECommendation system'

Author: Grafiati

Published: 26 October 2023

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Journal articles on the topic "COMmunity interest based RECommendation system"

1

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 (2017): 4187–202. http://dx.doi.org/10.1007/s11042-017-4553-9.

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Zheng, 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.

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Zheng, 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.

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Communities have become a popular platform of mining interests for recommender systems. The semantics of topics reflect users’ implicit interests. Sentiments on topics imply users’ sentimental tendency. People with common sentiments can form resonant communities of interest. In this paper, a resonant sentimental interest community-based recommendation model is proposed to improve the accuracy performance of recommender systems. First, we learn the weighted semantics vector and sentiment vector to model semantic and sentimental user profiles. Then, by combining semantic and sentimental factors,

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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 sm

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Zhou, 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 (2010): 1486–91. http://dx.doi.org/10.1609/aaai.v24i1.7524.

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Social tagging systems have emerged as an effective way for users to annotate and share objects on the Web. However, with the growth of social tagging systems, users are easily overwhelmed by the large amount of data and it is very difficult for users to dig out information that he/she is interested in. Though the tagging system has provided interest-based social network features to enable the user to keep track of other users' tagging activities, there is still no automatic and effective way for the user to discover other users with common interests. In this paper, we propose a User Recommend

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Gan, Mingxin, and Xiongtao Zhang. "Integrating Community Interest and Neighbor Semantic for Microblog Recommendation." International Journal of Web Services Research 18, no. 2 (2021): 54–75. http://dx.doi.org/10.4018/ijwsr.2021040104.

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As a typical characteristic of microblog information, short text length makes a microblog recommendation hard for new users. Moreover, user cold start makes it difficult to explore accurately the interests of microblog users. Therefore, the authors proposed a microblog recommendation model that integrates both of the users' interest from their communities and the semantic from their neighbors' microblogs. Based on the Kullback-Leibler (KL) language model, the proposed model estimated an interest-based language model and a microblog-based language model. Specifically, the interest-based languag

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Tang, 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.

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Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to

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Shokrzadeh, 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.

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Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses math

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Kumar, Akshi, and Saurabh Raj Sangwan. "Expert Finding in Community Question-Answering for Post Recommendation." International Journal of Engineering & Technology 7, no. 3.4 (2018): 151. http://dx.doi.org/10.14419/ijet.v7i3.4.16764.

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Community question answering system is a perfect example of platform where people participate to seek expertise on their topic of interest. But information overload, finding the expertise level of users and trustworthy answers remain key challenges within these communities. Moreover, people do not look for personal advices but expert views on such platforms therefore; expert finding is an integral part of these communities. In order to trust someone's opinion who is not known in person by the users of the community, it is necessary to find the credibility of such person. By determining experti

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10

Liu, Jing, and Yong Zhong. "Time-Weighted Community Search Based on Interest." Applied Sciences 12, no. 14 (2022): 7077. http://dx.doi.org/10.3390/app12147077.

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Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to sele

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