In recommender systems, accurate post-click conversion rate (CVR) estimation is crucial for a company’s revenue growth. Currently, multi-task causal methods—such as multi-task doubly robust learning—improve CVR estimation by reducing bias and variance during training. However, these methods often overlook the impact of hard-to-classify samples, which deviate significantly from the sample cluster center and substantially increase the bias and variance of multi-task causal approaches. To address this limitation, we propose a novel inverse contrastive representation learning guided causal multi-task framework. Specifically, we first select clicked and unclicked samples that deviate significantly from their respective cluster centers to form negative pairs. We then apply inverse contrastive representation learning (ICRL) to pull these negative pairs closer together, encouraging deviated clicked samples to move closer to the clicked cluster center, and vice versa. This approach yields a more accurate and lower-variance inverse propensity score for the CVR estimator. Additionally, we compute the ICRL loss segmentally to reduce computational resource consumption. We conduct extensive experiments on six large-scale datasets, as well as online A/B tests on our advertising platform. Our method achieves a 2.9% improvement on offline datasets compared to state-of-the-art baselines, and a 0.6% increase over the online production model.

In real-world industrial scenarios, post-click conversion rate (CVR) prediction models are trained offline based on click events and subsequently applied online to both clicked and unclicked events. Unfortunately, unclicked events are inevitably difficult to estimate due to user self-selection, which leads to a degradation of CVR prediction accuracy. In order to estimate the prediction of unclicked events, the current mainstream Doubly Robust (DR) estimators introduce the concept of imputed errors. However, inaccuracies in imputed errors can increase the uncertainty in the generalization bound of CVR predictions, consequently resulting in a decline in the CVR prediction accuracy. To challenge this issue, we first present a theoretical analysis of the bias and variance inherent in DR estimators and then introduce a novel causal estimator that seeks to strike a balance between bias and variance within the DR framework, thus optimizing the learning of the imputation model in a more robust manner. Additionally, drawing inspiration from adversarial learning techniques, we propose a novel dual adversarial component, which learns from both the space level and the task level to eliminate the causal influence of input features on the CTR task (i.e., the click propensity), with the goal of achieving unbiased estimations. Our extensive experimental evaluations, conducted on both the widely used benchmark and the real-world large-scale Internet giant platform, convincingly demonstrate the effectiveness of our proposed scheme. Besides, we have released a high-quality industrial dataset named Tenc-UnionAds used for selection bias research in the advertising field.

Recently hash-based nearest neighbor search has become attractive in many applications due to its compressed storage and fast query speed. However, the quantization in the hashing process usually degenerates its discriminative power when using Hamming distance ranking. To enable fine-grained ranking, hash bit weighting has been proved as a promising solution. Though achieving satisfying performance improvement, state-of-the-art weighting methods usually heavily rely on the projection's distribution assumption, and thus can hardly be directly applied to more general types of hashing algorithms. In this paper, we propose a new ranking method named QRank with query-adaptive bitwise weights by exploiting both the discriminative power of each hash function and their complement for nearest neighbor search. QRank is a general weighting method for all kinds of hashing algorithms without any strict assumptions. Experimental results on two well-known benchmarks MNIST and NUS-WIDE show that the proposed method can achieve up to 17.11\% performance gains over state-of-the-art methods.