Computational Advertising

Enlightenments, like accidents, happen only to prepared minds.

--Herbert Simon

Refinement for Online Advertisement

machine learning methods for improving online advertisement recommendations, e.g. CTR/CVR predictions.

Light componentized models and approaches for improving online advertisement recommendations are devised in this project. We also strive to provide users with better personalized recommendation. Some of recent research processes are summarized as follows.

Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback (SIGIR 2021, Best Short Paper Honorable Mention)

In this paper, we propose to tackle the delayed feedback problem in online advertising by “Following the Prophet” (FTP for short). The key insight is that, if the feedback came instantly for all the logged samples, we could get a model without delayed feedback, namely the “prophet”. Although the prophet cannot be obtained during online learning, we show that we could predict the prophet’s predictions by an aggregation policy on top of a set of multi-task predictions, where each task captures the feedback patterns of different periods. [paper]

GuideBoot: Guided Bootstrap for Deep Contextual Banditsin Online Advertising (WWW 2021)

In this paper, we introduce Guided Bootstrap (GuideBoot), which provides explicit guidance to the exploration behavior by training multiple models over both real and noisy samples with fake labels, where the noise is added according to the predictive uncertainty. The proposed method is efficient as it can make decisions on-the-fly by utilizing only one randomly chosen model, but is also effective as we show that it can be viewed as a non-Bayesian approximation of Thompson sampling. Moreover, we extend it to an online version that can learn solely from streaming data, which is favored in real applications. [paper]

Towards Explainable Conversational Recommendation (IJCAI 2020)

In this paper, we introduce explainable conversational recommendation, which enables incremental improvement of both recommendation accuracy and explanation quality through multi-turn usermodel conversation. We design an incremental multi-task learning framework that enables tight collaboration between recommendation prediction, explanation generation, and user feedback integration. We also propose a multi-view feedback integration method to enable effective incremental model update. Empirical results demonstrate that our model not only consistently improves the recommendation accuracy but also generates explanations that fit user interests reflected in the feedbacks. [paper]

Field-aware Calibration: A Simple and Empirically Strong
Method for Reliable Probabilistic Predictions (WWW 2020)

In this paper, we introduce a new evaluation metric named field-level calibration error that measures the bias in predictions over the sensitive input field that the decision-maker concerns. We then propose Neural Calibration, a simple yet powerful post-hoc calibration method that learns to calibrate by making full use of the field-aware information over the validation set.[paper]

Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings (SIGIR 2019)

In this work, we aim at improving the performance of CTR predictions during both the cold-start phase and the warm-up phase. We propose an approach coined Meta-Embedding that learns how to learn better embeddings for new ad IDs to address the cold-start problem. Then the embedding generator trained by the method can also speed up the model fitting and take the place of trivial random initializer for new ID embeddings so as to warm up cold-start for the new ad. [paper][code]

Attention-driven Factor Model for Explainable Personalized Recommendation (SIGIR 2018, short)

In this work, we propose the Attention-driven Factor Model (AFM), which can not only integrate item features driven by users’ attention but also can give reasonable explanations for users’ preferences and keep a high prediction accuracy. Meanwhile, we use the Gated Attention Units to extract explicit users’ preference. Taking advantage of rating and item features, the algorithm considers the personalization of different users' attention, and shows good efficiency and accuracy in experiments. [paper]