The Experts below are selected from a list of 17775 Experts worldwide ranked by ideXlab platform
Xiangsheng Huang - One of the best experts on this subject based on the ideXlab platform.
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a personalized e learning services Recommendation Algorithm based on user learning ability
International Conference on Advanced Learning Technologies, 2019Co-Authors: Zhengzhou Zhu, Qun Guo, Xiangsheng HuangAbstract:The E-learning services Recommendation is essential in enabling precision instruction and personalized learning. In this paper, a new personalized E-learning services Recommendation Algorithm is proposed to solve the problem of low accuracy, recall and effectiveness. The Algorithm builds user similarity matrix based on both user information data and user behavior data. In order to achieve the goal of bettering things, this paper creates an asymmetric similarity matrix based on the user learning ability and designs an E-learning services ranking strategy to make personalized E-learning service Recommendation better. The application of the Recommendation Algorithm in the personalized E-learning platform of a software college shows that the new Algorithm can improve the accuracy, recall and effectiveness compared with the traditional Recommendation Algorithm.
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ICALT - A Personalized E-Learning Services Recommendation Algorithm Based on User Learning Ability
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019Co-Authors: Honghao He, Xiangsheng HuangAbstract:The E-learning services Recommendation is essential in enabling precision instruction and personalized learning. In this paper, a new personalized E-learning services Recommendation Algorithm is proposed to solve the problem of low accuracy, recall and effectiveness. The Algorithm builds user similarity matrix based on both user information data and user behavior data. In order to achieve the goal of bettering things, this paper creates an asymmetric similarity matrix based on the user learning ability and designs an E-learning services ranking strategy to make personalized E-learning service Recommendation better. The application of the Recommendation Algorithm in the personalized E-learning platform of a software college shows that the new Algorithm can improve the accuracy, recall and effectiveness compared with the traditional Recommendation Algorithm.
Wei Wang - One of the best experts on this subject based on the ideXlab platform.
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an integrated tag Recommendation Algorithm towards weibo user profiling
Database Systems for Advanced Applications, 2015Co-Authors: Deqing Yang, Yanghua Xiao, Hanghang Tong, Junjun Zhang, Wei WangAbstract:In this paper, we propose a tag Recommendation Algorithm for profiling the users in Sina Weibo. Sina Weibo has become the largest and most popular Chinese microblogging system upon which many real applications are deployed such as personalized Recommendation, precise marketing, customer relationship management and etc. Although closely related, tagging users bears subtle difference from traditional tagging Web objects due to the complexity and diversity of human characteristics. To this end, we design an integrated Recommendation Algorithm whose unique feature lies in its comprehensiveness by collectively exploring the social relationships among users, the co-occurrence relationships and semantic relationships between tags. Thanks to deep comprehensiveness, our Algorithm works particularly well against the two challenging problems of traditional recommender systems, i.e., data sparsity and semantic redundancy. The extensive evaluation experiments validate our Algorithm’s superiority over the state-of-the-art methods in terms of matching performance of the recommended tags. Moreover, our Algorithm brings a broader perspective for accurately inferring missing characteristics of user profiles in social networks.
Gengxin Sun - One of the best experts on this subject based on the ideXlab platform.
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Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships
Mathematical Problems in Engineering, 2021Co-Authors: Sheng Bin, Gengxin SunAbstract:With the widespread use of social networks, social Recommendation Algorithms that add social relationships between users to recommender systems have been widely applied. Existing social Recommendation Algorithms only introduced one type of social relationship to the Recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization Recommendation Algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization Recommendation Algorithm has a significant improvement over the traditional and matrix factorization Recommendation Algorithms that integrate a single social relationship.
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A Novel Intelligent Recommendation Algorithm Based on Mass Diffusion
Discrete Dynamics in Nature and Society, 2020Co-Authors: Guanglai Tian, Shuang Zhou, Gengxin Sun, Chih-cheng ChenAbstract:Social Recommendation Algorithm is a common tool for recommending interesting or potentially useful items to users amidst the sea of online information. The users usually have various relationships, each of which has its unique impact on the Recommendation results. It is unlikely to make accurate Recommendations solely based on one relationship. Based on user-item bipartite graph, this paper establishes a multisubnet composited complex network (MSCCN) of multiple user relationships and then extends the mass diffusion (MD) Algorithm into a novel intelligent Recommendation Algorithm. Two public online datasets, namely, Epinions and FilmTrust, were selected to verify the effect of the proposed Algorithm. The results show that the proposed intelligent Recommendation Algorithm with two types of relationships made much more accurate Recommendations than that with a single relationship and the traditional MD Algorithm.
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Matrix Decomposition Recommendation Algorithm Based on Multiple Social Relationships
2020 IEEE 2nd Eurasia Conference on Biomedical Engineering Healthcare and Sustainability (ECBIOS), 2020Co-Authors: Cuijuan Gong, Gengxin Sun, Chi-cheng Chen, Sheng BinAbstract:A real society has multiple social relationships between users, but existing social network Recommendation Algorithms often only introduce a social relationship into the Recommendation system. This paper introduces a variety of social relationships into the Recommendation system based on a multi-subnet composite complex network model. Based on the analysis of the experimental results on the Epinions dataset, a Recommendation Algorithm introducing multiple social relationships has a significantly higher Recommendation accuracy than a Recommendation Algorithm.
Ni Wenye - One of the best experts on this subject based on the ideXlab platform.
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Network Representation Learning Enhanced Recommendation Algorithm
IEEE Access, 2019Co-Authors: Qiang Wang, Haiyan Gao, Li Zhang, Yang Cao, Lin Mao, Dou Kaiqi, Ni WenyeAbstract:With the popularity of social network applications, more and more recommender systems utilize trust relationships to improve the performance of traditional Recommendation Algorithms. Social-network-based Recommendation Algorithms generally assume that users with trust relations usually share common interests. However, the performance of most of the existing social-network-based Recommendation Algorithms is limited by the coarse-grained and sparse trust relationships. In this paper, we propose a network representation learning enhanced Recommendation Algorithm. Specifically, we first adopt a network representation technique to embed social network into a low-dimensional space, and then utilize the low-dimensional representations of users to infer fine-grained and dense trust relationships between users. Finally, we integrate the fine-grained and dense trust relationships into the matrix factorization model to learn user and item latent feature vectors. The experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based Recommendation Algorithms.
Sheng Bin - One of the best experts on this subject based on the ideXlab platform.
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Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships
Mathematical Problems in Engineering, 2021Co-Authors: Sheng Bin, Gengxin SunAbstract:With the widespread use of social networks, social Recommendation Algorithms that add social relationships between users to recommender systems have been widely applied. Existing social Recommendation Algorithms only introduced one type of social relationship to the Recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization Recommendation Algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization Recommendation Algorithm has a significant improvement over the traditional and matrix factorization Recommendation Algorithms that integrate a single social relationship.
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Matrix Decomposition Recommendation Algorithm Based on Multiple Social Relationships
2020 IEEE 2nd Eurasia Conference on Biomedical Engineering Healthcare and Sustainability (ECBIOS), 2020Co-Authors: Cuijuan Gong, Gengxin Sun, Chi-cheng Chen, Sheng BinAbstract:A real society has multiple social relationships between users, but existing social network Recommendation Algorithms often only introduce a social relationship into the Recommendation system. This paper introduces a variety of social relationships into the Recommendation system based on a multi-subnet composite complex network model. Based on the analysis of the experimental results on the Epinions dataset, a Recommendation Algorithm introducing multiple social relationships has a significantly higher Recommendation accuracy than a Recommendation Algorithm.
