The content ranking according to users′ browsing preference in content recommendation plays an important role in improvement of the user clicks rate. The content in recommendation flow changes with time. The clicks information of user and recommendation content in historical data is analyzed for regression analysis. The feature correlation while user clicks the content is extracted. The features are normalized to fit the clicks rate of current features distribution. The linear regression is used as the fitting model to predict user clicks rate. The logs browsed and clicked by users are taken as the test dataset in the ex-periment. The content clicked by users in the single field is cut out with the improved algorithm as the clicks rate for verifica-tion. The experimental results show that the improved algorithm can recommend clicks content of user preference accurately.%在内容推荐中根据用户的浏览偏好进行内容排序对提高用户的点击率具有至关重要的作用.推荐流中内容随着时间变化呈现出流动性,分析历史数据中用户和推荐内容的点击信息进行回归分析,提取用户在内容点击时特征的相关性,并对特征进行归一化,拟合出在当前特征分布下的点击率,以线性回归作为拟合模型进行用户点击率预测.实验以今日头条中用户浏览点击日志为测试数据集,采用改进算法进行内容排序时截取单领域下用户点击内容作为点击率进行验证,实验结果表明,改进算法能够较为准确地推荐用户倾向点击的内容.
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