ORIC: Feature Interaction Detection through Online Random Interaction Chains for Click-Through Rate Prediction

Click-through rate prediction aims to predict the ratio of clicks to impressions of a specific link, which is challenging due to (1) extremely high-dimensional categorical features; (2) both important original features and their interactions; and (3) reliance on different features and interactions in different time periods. To overcome these difficulties, we propose a new feature interaction detection method based on the idea of frequent itemset mining, named Online Random Intersection Chains (ORIC), which detects informative feature interactions with high interpretability. ORIC can be updated by controlling the importance of the historical and latest data with a tuning parameter, which saves computational burden and makes full use of historical information. Further, Streaming Integrated Model (SIM) is developed to feed the time-varying feature interactions into CTR prediction models. Empirical results on three benchmark datasets show that SIM achieves better performance than many CTR prediction models, as well as the efficiency, consistency, and interpretability of ORIC.