基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。

Ranking Algorithm of Search Engine Based on Users Feedback

This paper used user behavior in Web 2.0 as a research object,explored ways of user feedback,and proposed the concept of user feedback score.It studied the specific methods and corresponding realization for user feedback impacting the final ranking of search results,and presented a sorting algorithm for search results based on neural network.The algorithm used the BP neural network model,select samples to train the neural network based on user feedback score.Traditional search results will be put into the trained neural network to compute,and a new ranking will be made according to relevance of the web page which indicated by the calculated results.This algorithm used the neural network’s pattern recognition capabilities,combined user feedback and search engine effectively,making search results more in line with the user,s search request.