In this article, we investigate a novel setting for offline policy enhancement and transfer that involves two distinct datasets: an experimental dataset and an external one-sided treatment dataset. The experimental dataset, though unconfounded, is constrained by its small sample. Consequently, methods based solely on the experimental dataset may suffer from low accuracy and limited generalizability. In contrast, the external one-sided treatment dataset typically has a larger sample size but includes observations from only one treatment arm (e.g., all units belong to the control group, with no units receiving the treatment). Based only on the external one-sided treatment dataset, it cannot identify the policy reward. By combining the two datasets, we propose a principled framework to accomplish two key tasks: (1) policy enhancement: improving the accuracy of offline policy evaluation and learning in the experimental dataset by leveraging the external one-sided treatment dataset; (2) policy transfer : enabling offline policy evaluation and learning in the external one-sided treatment dataset by utilizing information from the experimental dataset, and thus making the learned policies applicable to a broader range of data distributions. Extensive experiments demonstrate that our proposed methods not only estimate rewards more accurately but also learn policies that closely approximate the theoretically optimal policy. The code is available at https://anonymous.4open.science/r/Offline-Policy-Enhancement-and-Transfer-E0D1 for double-blind review.

In the realm of recommender systems (RS), Top-K ranking metrics such as NDCG@K are the gold standard for evaluating recommendation performance. However, during the training of recommendation models, optimizing NDCG@K poses significant challenges due to its inherent discontinuous nature and the intricate Top-K truncation. Recent efforts to optimize NDCG@K have either overlooked the Top-K truncation or suffered from high computational costs and training instability. To overcome these limitations, we propose SoftmaxLoss@K (SL@K), a novel recommendation loss tailored for NDCG@K optimization. Specifically, we integrate the quantile technique to handle Top-K truncation and derive a smooth upper bound for optimizing NDCG@K to address discontinuity. The resulting SL@K loss has several desirable properties, including theoretical guarantees, ease of implementation, computational efficiency, gradient stability, and noise robustness. Extensive experiments on four real-world datasets and three recommendation backbones demonstrate that SL@K outperforms existing losses with a notable average improvement of 6.03%. The code is available at https://github.com/Tiny-Snow/IR-Benchmark.

In database of recommender systems, users' ratings for most items are usually missing, resulting in selection bias when users selectively choose items to rate. To address this problem, propensity-based methods, e.g., inverse propensity scoring and doubly robust, have been widely studied and applied to missing rating prediction and post-click conversion rate prediction tasks. However, have we completely eliminated the selection bias? Under what missing data mechanism can previous studies completely eliminate the selection bias and lead to unbiased learning? In this paper, following the previous literature on statistics, we first formally define three missing data mechanisms, i.e., missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), and discuss the widespread prevalence of MNAR in recommender systems. Next, we theoretically reveal that the unbiasedness of previous propensity-based debiasing methods is valid only when data are MCAR or MAR, while it leads to biased predictions when data are MNAR. To tackle this research gap, we propose to disentangle user and item embeddings into the primary latent vector for rating prediction and the auxiliary latent vector for missing mechanism modeling. We prove the identifiablility results, and show that the proposed method can achieve unbiased learning under MNAR with imposed constraints. Extensive experiments are conducted on a semi-synthetic dataset and three real-world datasets, validating the effectiveness of our proposed method.

Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically mitigate the bias, increase recommendation diversity, or use bandit algorithms to balance exploration-exploitation trade-offs. Nevertheless, they fail to consider the potential rewards of recommending different categories of items and lack the global scheduling of allocating top-N recommendations to categories, leading to suboptimal exploration. In this work, we propose an Uplift model-based Recommender (UpliftRec) framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards and leverages inverse propensity weighting to alleviate confounder bias. Thereafter, UpliftRec adopts a dynamic programming method to calculate the optimal treatment for overall CTR maximization. We implement UpliftRec on different backend models and conduct extensive experiments on three datasets. The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.

Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.

Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction.

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.

Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues in a range of industrial applications. One of the most challenging problems of this task is the existence of severe selection bias caused by the inherent self-selection behavior of users and the item selection process of systems. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice. Motivated by such analysis, we propose a generalized learning framework that not only unifies existing DR methods, but also provides a valuable opportunity to develop a series of new debiasing techniques to accommodate different application scenarios. Based on the framework, we propose two new DR methods, namely DR-BIAS and DR-MSE. DR-BIAS directly controls the bias of DR loss, while DR-MSE balances the bias and variance flexibly, which achieves better generalization performance. In addition, we propose a novel tri-level joint learning optimization method for DR-MSE in CVR prediction, and an efficient training algorithm correspondingly. We conduct extensive experiments on both real-world and semi-synthetic datasets, which validate the effectiveness of our proposed methods.

Recommender systems should answer the intervention question "if recommending an item to a user, what would the feedback be", calling for estimating the causal effect of a recommendation on user feedback. Generally, this requires blocking the effect of confounders that simultaneously affect the recommendation and feedback. To mitigate the confounding bias, a strategy is incorporating propensity into model learning. However, existing methods forgo possible unmeasured confounders (e.g., user financial status), which can result in biased propensities and hurt recommendation performance. This work combats the risk of unmeasured confounders in recommender systems.