基于贝叶斯推理的点击模型及其实现

CLICK MODEL BASED ON BAYESIAN INFERENCE AND ITS IMPLEMENTATION

为能更好地解释搜索引擎和商务搜索的点击日志中的用户行为,实现一种用于分析日志中包含的用户行为的贝叶斯点击模型。通过分析中国最大电子商务网站的约927万条用户搜索点击日志数据,发现一个的文档的点击是受其上下位置点击过的文档共同影响的,然后基于此发现提出并实现一种新的基于贝叶斯推理的点击模型,并给出并行版本的算法实现。最后通过利用来自用户搜索的一个月日志数据验证,结果表明该模型优于现有的点击模型。

In order to better explain user behaviour from click logs in search engine or sponsored search, we implement a Bayesian click model for analysing user behaviours included in logs. By analysing about 9.27 million click log data collected from a largest e-commerce site of China, there finds that the click probability of a document is affected by the clicked documents above and below it. Then we propose and implement a new click model based on Bayesian inference according to the phenomenon found, together with the implementation of an algorithm in parallel version. At last, we validate the model through a log data set collected about a mouth from user search, and the result shows that the proposed model outperforms existing click models.