CAJ | 학술논문

上下文广告与用户兴趣及网页内容相匹配,可增强用户体验并提高广告点击率.而广告收益与广告点击率直接相关,准确预测广告点击率是提高上下文广告收益的关键.目前,上下文广告推荐面临如下问题:(1)网页数量及用户数量规模很大;(2)历史广告点击数据十分稀疏,导致点击率预测准确率低.针对上述问题,提出一种基于联合概率矩阵分解的因子模型 AdRec,它结合用户、广告和网页三者信息进行广告推荐,以解决数据稀疏时点击率预测准确率低的问题.算法复杂度随着观测数据数量的增加呈线性增长,因此可应用于大规模数据.
상하문엄고여용호흥취급망혈내용상필배,가증강용호체험병제고엄고점격솔.이엄고수익여엄고점격솔직접상관,준학예측엄고점격솔시제고상하문엄고수익적관건.목전,상하문엄고추천면림여하문제:(1)망혈수량급용호수량규모흔대;(2)역사엄고점격수거십분희소,도치점격솔예측준학솔저.침대상술문제,제출일충기우연합개솔구진분해적인자모형 AdRec,타결합용호、엄고화망혈삼자신식진행엄고추천,이해결수거희소시점격솔예측준학솔저적문제.산법복잡도수착관측수거수량적증가정선성증장,인차가응용우대규모수거.
@@@@Combining user interests with visited web page contents to perform contextual advertising enhances the user experience and adds more ad clicks, increasing revenue. The key issue is to improve the prediction accuracy of click rates for advertisements. The crucial challenges of the advertisement recommendation algorithm are the scalability on large number of users and web page contents, and the low prediction accuracy resulting from data sparsity. When data is large and sparse, the accuracy and efficiency of the traditional recommendation algorithms is poor. This paper proposes a factor model called AdRec. Based on the Unified Probability Matrix Factorization (UPMF), the model addresses the data sparsity problem by combining features of users, advertisements and web page contents to predict the click rate with higher accuracy. In addition, the computational complexity of our algorithm is linear with respect to the number of observed data, and scalable to very large datasets.