基于多粒度特征和混合算法的文档推荐系统

摘要

文库系统对信息的传播利用有着重要的作用, 但在文库系统中出现信息过载问题后, 数据的利用率会大大降低. 针对该问题提出了一种基于多粒度特征和混合算法的文档推荐系统, 系统在短语和词语两个粒度上对用户兴趣及文档特征进行建模, 综合基于内容推荐算法及协同过滤算法, 为用户生成兴趣列表. 系统测试数据表明, 系统在准确率、召回率、覆盖率、新颖度等指标上均有较为优异的表现, 其为用户推荐的文档较符合用户实际偏好, 有助于提升文库系统的数据利用率, 改善用户体验.%Document System plays an important role in information dissemination and utilization. However, with the emergence of information overload, the utilization rate of data would greatly decrease. To solve this problem, a document recommendation system based on multi-granularity features and Hybrid Algorithms is proposed. User interest and document feature models are established on both phrase and term granularities. Then, the system generates recommendation lists for users based on the combination of content-based and collaborative-filtering algorithms. The tests based on authentic data demonstrate that the document recommendation system has a better performance on precision, recall rate, coverage rate and novelty. The recommendation lists are more in line with users' interests. This helps to increase the utilization rate of data and improves user experience with better performance.