Click-through rate prediction is a critical task in online advertising. Currently, many existing methods attempt to extract user potential interests from historical click behavior sequences. However, it is difficult to handle sparse user behaviors or broaden interest exploration. Recently, some researchers incorporate the item-item co-occurrence graph as an auxiliary. Due to the elusiveness of user interests, those works still fail to determine the real motivation of user click behaviors. Besides, those works are more biased towards popular or similar commodities. They lack an effective mechanism to break the diversity restrictions. In this paper, we point out two special properties of triangles in the item-item graphs for recommendation systems: Intra-triangle homophily and Inter-triangle heterophiy. Based on this, we propose a novel and effective framework named Triangle Graph Interest Network (TGIN). For each clicked item in user behavior sequences, we introduce the triangles in its neighborhood of the item-item graphs as a supplement. TGIN regards these triangles as the basic units of user interests, which provide the clues to capture the real motivation for a user clicking an item. We characterize every click behavior by aggregating the information of several interest units to alleviate the elusive motivation problem. The attention mechanism determines users' preference for different interest units. By selecting diverse and relative triangles, TGIN brings in novel and serendipitous items to expand exploration opportunities of user interests. Then, we aggregate the multi-level interests of historical behavior sequences to improve CTR prediction. Extensive experiments on both public and industrial datasets clearly verify the effectiveness of our framework.
翻译:点击率预测是在线广告中的关键任务。 目前, 许多现有方法试图从历史点击行为序列中提取用户潜在兴趣。 但是, 很难处理稀少的用户行为, 或扩大兴趣探索范围。 最近, 一些研究人员将项目项目项目项目共同点图作为辅助。 由于用户兴趣的模糊性, 这些作品仍然无法确定用户点击行为的真实动机。 此外, 这些作品更偏向于用户点击行为的真实动机。 它们缺乏打破多样性限制的有效机制 。 在本文中, 我们指出建议系统的项目项目项目水平图表中的三角的两种特殊特性: 异端的同源和三角异端异端异端异端。 基于此, 我们提议了一个新颖而有效的框架, 名为“ 三角图形利益网络 ” ( TGIN ) 。 在用户行为序列中点击每个项目时, 我们用其周围的三角关系作为补充。 TGIN 将这些三角关系视为用户兴趣的基本单位, 提供一些线索, 用来捕捉到用户的真实动机, 并用一个项目来测量其真实性, 我们通过每组的动机, 选择一个数字级的动机, 来决定我们不同的勘探机会的动机 。