提出InterFormer模型，提升点击率预测中异构信息的双向交互。
InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
- 采用双向信息流设计，实现多模态信息互惠学习。
- 在三个公开数据集和工业数据集上均达到领先性能。
- 适合需要精准用户兴趣建模的推荐系统开发者。
点击率（CTR）预测是推荐系统中的核心任务，旨在预估用户点击广告的概率。随着用户画像与行为序列等异构信息的引入，从多个维度刻画用户兴趣成为可能。然而，现有方法存在两大缺陷：一是模式间信息流动单向，交互不足；二是过早聚合导致信息过度损失。为此，本文提出InterFormer模块，以交错式方式学习异构信息的交互。通过双向信息流实现跨模态互惠学习，并保留各数据模态的完整信息，使用独立桥接结构进行有效选择与摘要。所提方法在三个公开数据集及一个大规模工业数据集上均取得当前最优性能。
原文摘要 · Abstract (English)
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction. However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address the above limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. To achieve better interaction learning, InterFormer enables bidirectional information flow for mutually beneficial learning across different modes. To avoid aggressive information aggregation, we retain complete information in each data mode and use a separate bridging arch for effective information selection and summarization. Our proposed InterFormer achieves state-of-the-art performance on three public datasets and a large-scale industrial dataset.
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