Personalized search has been a hot research topic for many years and has been widely used in e-commerce. This paper describes our solution to tackle the challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this competition is to predict search relevance and re-rank the result items in SERP according to the personalized search, browsing and purchasing preferences. Based on a detailed analysis of the provided data, we extract three different types of features, i.e., statistic features, query-item features and session features. Different models are used on these features, including logistic regression, gradient boosted decision trees, rank svm and a novel deep match model. With the blending of multiple models, a stacking ensemble model is built to integrate the output of individual models and produce a more accurate prediction result. Based on these efforts, our solution won the champion of the competition on all the evaluation metrics.
翻译:个人化搜索是多年来一个热门的研究课题,在电子商务中广泛使用。本文描述了我们应对2016年CIKM杯个人化电子商务搜索挑战的解决方案。这一竞争的目的是根据个人化搜索、浏览和购买偏好预测搜索的相关性和重新排列SERP中的结果项目。根据对所提供的数据的详细分析,我们提取了三种不同类型的特征,即统计特征、查询项目特征和会话特征。在这些特征上使用了不同的模型,包括物流回归、梯度增强决策树、等级标准以及新的深度匹配模型。随着多种模型的混合,将建立一个堆叠式集式模型,整合单个模型的输出并产生更准确的预测结果。基于这些努力,我们的解决办法赢得了所有评价指标的竞争对手。