Rich user behavior information is of great importance for capturing and understanding user interest in click-through rate (CTR) prediction. To improve the richness, collecting long-term behaviors becomes a typical approach in academy and industry but at the cost of increasing online storage and latency. Recently, researchers have proposed several approaches to shorten long-term behavior sequence and then model user interests. These approaches reduce online cost efficiently but do not well handle the noisy information in long-term user behavior, which may deteriorate the performance of CTR prediction significantly. To obtain better cost/performance trade-off, we propose a novel Adversarial Filtering Model (ADFM) to model long-term user behavior. ADFM uses a hierarchical aggregation representation to compress raw behavior sequence and then learns to remove useless behavior information with an adversarial filtering mechanism. The selected user behaviors are fed into interest extraction module for CTR prediction. Experimental results on public datasets and industrial dataset demonstrate that our method achieves significant improvements over state-of-the-art models.
翻译:丰富的用户行为信息对于捕捉和理解用户对点击通速(CTR)预测的兴趣非常重要。为了改进丰富度,收集长期行为在学院和行业中成为典型的做法,但以增加在线存储和潜伏为代价。最近,研究人员提出了几种方法来缩短长期行为序列,然后模拟用户兴趣。这些方法可以降低在线成本效率,但不能很好地处理长期用户行为中的吵闹信息,这可能会大大恶化CTR预测的绩效。为了获得更好的成本/性能权衡,我们提议了一个新的反逆过滤模式(ADFM)来模拟长期用户行为模式。ADFM使用等级汇总代表来压缩原始行为序列,然后学会用对抗过滤机制删除无用的行为信息。选定的用户行为被输入到CTR预测的提取模块中。公共数据集和工业数据集的实验结果表明,我们的方法比最新模型取得了显著的改进。