基于内容过滤推荐的农业信息推荐模型研究

Research on agricultural information recommendation models based on content filtering

针对专门的农业知识库,使用基于内容过滤的推荐方法,建立了农民用户兴趣模型和文档特征模型。在用户兴趣模型和文档特征模型中,针对特征项在不同表空间的分布情况,以及HTML文档结构对特征项权重的影响,通过改进传统特征项提取算法,提高了推荐模型的精度。结果表明,随着用户数的增加,农业信息推荐模型的查准率和查全率不断加大,说明模型的精确度不断提高。

The adaptive recommendation models in the agriculture information service platform are very important, which provides personalized recommendation information to farmers. Aiming at the special agricultural knowledge bases, by using the recommended methods based on content filtering, we have established the farmer interest model and the document feature model. In the two models, taking account of the influence of distribution of features in the different table space and the effect of HTML document structure on the feature weights, we have improved the accuracy of recommendation model by improving the traditional feature extraction algorithm. The experimental results show that with the increasing number of users of agricultural information recommendation models, the precision and recall rate of them are also increasing, the accuracy of them are also rising.