一种基于因子图的搜索广告转化预测模型

基于转化的广告方式在应用和研究中逐渐得到重视,采用该方式的搜索广告在广告排序时需要对候选广告的转化概率进行预测,以提高广告的转化率,优化搜索引擎的广告收益。该文在对搜索广告中影响转化的各特征进行提取与分析的基础上,提出了描述广告、查询、用户三个因素与转化事件关系的概率因子图模型,并基于该模型对广告转化进行预测。最后我们使用从某商业搜索引擎采集的实际数据对预测模型进行评价并与朴素贝叶斯方法进行对比,实验结果表明,三类因素对转化具有不同程度的影响,我们提出的因子图模型可以较好地预测广告的转化。

A Factor Graph Based Conversion Prediction Model for Sponsored Search

The CPA (Cost-per-Action) Advertising is attracting more and more attention in both industry and research. Sponsored search based on CPA requires predicting conversion probability for each candidate ad during ad ranking, in order to raise conversion rate and optimize ad revenue for search engine. After extracting and analyzing features which may influence conversion of ads, we propose a probabilistic factor graph based model for ad conversion prediction which describes the relation between the conversion event and three factors, i.e. ad, query, and user. The model is evaluated and compared with Naive Bayesian method on real-world data gathered from a commercial search engine. The experiment demonstrates a good result in the ad conversion prediction, as well as different influences of the three factors.