摘要
为了克服传统检索算法在个性化检索上的不足,提出了基于蚁群算法的资源检索模块。该模块挖掘Web日志中的用户向量,根据向量的相关度寻找当前用户的邻近用户。模拟蚁群算法建立概率模型,并按照概率值对资源进行降序排列,将结果提供给用户作为决策支持。实验表明新的检索模块优化了资源检索过程,提高了检索效率,实现了个性化网络教学资源检索。最后分析了模块的优越性和局限性,并对以后的发展方向进行了展望。
To overcome the shortage of traditional algorithm in personalized retrieval, this paper presents a resources retrieval module based on ant colony optimization （ACO） algorithm. This module mines the user＇s vector in the web log, searches the current user＇s neighbouring users by using the correlation of vector. It simulates ACO algorithms and constitute probability model, arrays all resources in descending order according to probability value, provides the results for users as the decision-making supports. The experimental results indicate that the new retrieval module optimizes the process of resources retrieval, improves the retrieval efficiency, and realizes personalized network education resource retrieval. Finally, the advantages and disadvantages of module are analysed, the prospects for further research are discussed.
出处
《计算机应用与软件》
CSCD
北大核心
2008年第11期197-198,213,共3页
Computer Applications and Software
关键词
网络教育资源
个性化检索
蚁群算法
评价
Network education resource Personalized retrieval Ant colony optimization Evaluate
