共被期刊论文引用了2次
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| 1 | A New Recommender System Using Context Clustering Based on Matrix Factorization Techniques显示文摘Recommender system can efficiently alleviate the information overload problem, but it has been trapped in the recommendation accuracy. We proposed a new recommender system which based on matrix factorization techniques. More factors including contextual information, user ratings and item feature are all taken into consideration. Meanwhile the k-modes algorithm is used to reduce the complexity of matrix operations and increase the relevance of the user-item ratings sub-matrix.Compared with several ma jor existing recommendation approaches, extensive experimental evaluation on publicly available dataset demonstrates that our method enjoys improved recommendation accuracy. | ZHENG Xiaoyao LUO Yonglong SUN Liping CHEN Fulong | 2016 | Chinese Journal of Electronics2016，25，2： | 4 |
| 2 | 一种基于Skill-LFM的知识点推荐方法显示文摘目前,知识库的用户主要是通过检索获取所需知识点,这种依赖搜索引擎解决信息过载的方法,对实时在线服务而言效率低下,对离线知识学习来说不具有完整性和连续性,为此提出由知识库系统根据用户技能水平主动推荐知识点给用户,提高决策效率,并有助于用户建立完备的知识学习体系.基于用户对知识点的历史行为以及用户对知识的学习能力,提出一种融合技能的隐语义模型的协同过滤推荐方法,将知识点难易程度作为潜在因子,同时考虑用户的能力水平预测用户对知识点的偏好水平.在呼叫中心知识库的数据集上进行测试,其均方根误差优于基础隐语义模型.综合知识点推荐的应用领域和知识学习行为数据的特点,对于知识点推荐方法,可从融合用户和知识点上下文信息的推荐技术上深入研究. | 方建生 许言午 蔡瑞初 秦艳 | 2018 | 中国科学技术大学学报2018，48，9： | 1 |
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