Both item association and user association can be used to recommend for the current user in Rule-based recommendation.
基于规则的推荐技术在数据集上挖掘项目关联和用户关联为当前用户做推荐。
The experiments show that, comparing with the recommendation algorithms based on association rule or on user transaction, the algorithm precision is improved greatly.
实验表明，该算法比使用基于关联规则和基于用户事务的推荐算法的精确性有较大幅度的提高。
The system gives two kinds of recommendation algorithms based on association rule mining and user's transaction pattern clustering.
本系统给出了基于关联规则挖掘和基于用户事务模式聚类两种推荐算法。
The news recommendation rule generation part is based on feature extraction part and it is the complement of the system.
新闻推荐规则是建立在用户特征提取算法的基础之上实现的，是对本系统实现的补充和完善。
A algorithm for generating recommendation rule set is proposed based on the users browsing interest, which is measured by considering synth.
提出了一个基于用户浏览兴趣的推荐规则集生成算法，在度量用户浏览兴趣时综合考虑了用户浏览时间和对该页面的访问次数。
A algorithm for generating recommendation rule set is proposed based on the users browsing interest, which is measured by considering synth.
提出了一个基于用户浏览兴趣的推荐规则集生成算法，在度量用户浏览兴趣时综合考虑了用户浏览时间和对该页面的访问次数。
应用推荐