Disentangled representation learning aims to learn low-dimensional representations where each dimension corresponds to an underlying generative factor. While the Variational Auto-Encoder (VAE) is widely used for this purpose, most existing methods assume independence among factors, a simplification that does not hold in many real-world scenarios where factors are often interdependent and exhibit causal relationships. To overcome this limitation, we propose the Disentangled Causal Variational Auto-Encoder (DCVAE), a novel supervised VAE framework that integrates causal flows into the representation learning process, enabling the learning of more meaningful and interpretable disentangled representations. We evaluate DCVAE on both synthetic and real-world datasets, demonstrating its superior ability in causal disentanglement and intervention experiments. Furthermore, DCVAE outperforms state-of-the-art methods in various downstream tasks, highlighting its potential for learning true causal structures among factors.

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.

Predicting the probability that a user clicks a specific item is fundamental in online advertising and recommendation. Further, it is crucial to use the latest and historical data appropriately in online scenarios to train CTR models. Online Random Interaction Chains (ORIC) was proposed to detect informative and interpretable feature interactions without retraining on historical data in online scenario, and the Streaming Integrated Model (SIM) framework was designed to integrate these time-varying feature interactions into CTR prediction models. Unfortunately, ORIC exhibits latency when provides the feature interactions used to evaluate SIM, and ORIC is not applicable for numerical features. For these reasons, we propose ORIC-V2 that uses time series models to predict the confidence of candidate evaluating feature interactions and selects reasonable feature interactions, and combines numerical features with ORIC-V2 through a discretization model to obtain DORIC-V2. Feeding the feature interactions found by ORIC-V2 and DORIC-V2 into SIM obtains significant experimental results on three datasets, demonstrating the effectiveness and interpretability of ORIC-V2 and DORIC-V2.