Despite the impressive generative capabilities of text-to-image (T2I) models, accurate binding of objects and attributes specified in input prompts remains a significant challenge. Existing approaches often fail to address inherent biases in text embeddings, where objects tend to associate with their frequent attributes in the training data. This work identifies these biases and introduces a novel debiasing framework centered on alternating prompt vector binding, dynamically improving semantic alignment during image generation. First, we quantify bias strength in object-attribute pairs using large language models (LLMs), revealing problematic associations. Next, biased object terms are replaced with broader concepts during diffusion sampling to reduce bias. Finally, object embeddings are refined by pulling target attributes while suppressing irrelevant ones, avoiding attribute confusion. Extensive experiments on public datasets demonstrate the framework’s effectiveness in addressing attribute binding challenges through bias mitigation.

Recommender systems have been widely employed on various online platforms to improve user experience. In these systems, recommendation models are often learned from the users' historical behaviors that are automatically collected. Notably, recommender systems differ slightly from ordinary supervised learning tasks. In recommender systems, there is an exposure mechanism that decides which items could be presented to each specific user, which breaks the i.i.d assumption of supervised learning and brings biases into the recommendation models. In this paper, we focus on unbiased ranking loss weighted by inversed propensity scores (IPS), which are widely used in recommendations with implicit feedback labels. More specifically, we first highlight the fact that there is a gap between theory and practice in IPS-weighted unbiased loss. The existing pairwise loss could be theoretically unbiased by adopting an IPS weighting scheme. Unfortunately, the propensity scores are hard to estimate due to the inaccessibility of each user-item pair's true exposure status. In practical scenarios, we can only approximate the propensity scores. In this way, the theoretically unbiased loss would be still practically biased. To solve this problem, we first construct a theoretical framework to obtain a generalization upper bound of the current theoretically unbiased loss. The bound illustrates that we can ensure the theoretically unbiased loss's generalization ability if we lower its implementation loss and practical bias at the same time. To that aim, we suggest treating feedback label as a noisy proxy for exposure result for each user-item pair . Here we assume the noise rate meets the condition that . According to our analysis, this is a mild assumption that can be satisfied by many real-world applications. Based on this, we could train an accurate propensity model directly by leveraging a noise-resistant loss function. Then we could construct a practically unbiased recommendation model weighted by precise propensity scores. Lastly, experimental findings on public datasets demonstrate our suggested method's effectiveness.

To improve user experience, recommender systems have been widely used on many online platforms. In these systems, recommendation models are typically learned from positive/negative feedback that are collected automatically. Notably, recommender systems are a little different from general supervised learning tasks. In recommender systems, there are some factors (e.g., previous recommendation models or operation strategies of a online platform) that determine which items can be exposed to each individual user. Normally, the previous exposure results are not only relevant to the instances' features (i.e., user or item), but also affect their feedback ratings, thus leading to confounding bias in the recommendation models. To mitigate this bias, researchers have already provided a variety of strategies. However, there are still two issues that are underappreciated: 1) previous debiased RS approaches cannot effectively capture recommendation-specific, exposure-specific and their common knowledge simultaneously; 2) the true exposure results of the user-item pairs are partially inaccessible, so there would be some noises if we use their observability to approximate it as existing approaches. Motivated by this, we develop a novel debiasing recommendation approach. More specifically, we first propose a mutual information-based counterfactual learning framework based on the causal relationship among the instance features, exposure status, and ratings. This framework can 1) capture recommendation-specific, exposure-specific and their common knowledge by explicitly modeling the relationship among the causal factors, and 2) achieve robustness towards partially inaccessible exposure results by a pairwise learning strategy. Under such a framework, we implement an optimizable loss function with theoretical analysis. By minimizing this loss, we expect to obtain an unbiased recommendation model that reflects the users' real interests. Meanwhile, we also prove that our loss function has robustness towards the partial inaccessibility of the exposure status. Finally, extensive experiments on public datasets manifest the superiority of our proposed method in boosting the recommendation performance.

Click-through rate (CTR) prediction, whose goal is to predict the probability of the user to click on an item, has become increasingly significant in the recommender systems. Recently, some deep learning models with the ability to automatically extract the user interest from his/her behaviors have achieved great success. In these work, the attention mechanism is used to select the user interested items in historical behaviors, improving the performance of the CTR predictor. Normally, these attentive modules can be jointly trained with the base predictor by using gradient descents. In this paper, we regard user interest modeling as a feature selection problem, which we call user interest selection. For such a problem, we propose a novel approach under the framework of the wrapper method, which is named Meta-Wrapper. More specifically, we use a differentiable module as our wrapping operator and then recast its learning problem as a continuous bilevel optimization. Moreover, we use a meta-learning algorithm to solve the optimization and theoretically prove its convergence. Meanwhile, we also provide theoretical analysis to show that our proposed method 1) efficiencies the wrapper-based feature selection, and 2) achieves better resistance to overfitting. Finally, extensive experiments on three public datasets manifest the superiority of our method in boosting the performance of CTR prediction.

Nowadays, click-through rate (CTR) prediction has achieved great success in online advertising. However, making desirable predictions for unseen ads is still challenging, which is known as the cold-start problem. To address such a problem in CTR prediction, meta-learning methods have recently emerged as a popular direction. In these approaches, the predictions for each user/item are regarded as individual tasks, then training a meta-learner on them to implement zero-shot/few-shot learning for unknown tasks. Though these approaches have effectively alleviated the cold-start problem, two facts are not paid enough attention, 1) the diversity of the task difficulty and 2) the perturbation of the task distribution. In this paper, we propose an adaptive loss that ensures the consistency between the task weight and difficulty. Interestingly, the loss function can also be viewed as a description of the worst-case performance under distribution perturbation. Moreover, we develop an algorithm, under the framework of gradient descent with max-oracle (GDmax), to minimize such an adaptive loss. Then we prove the algorithm can return to a stationary point of the adaptive loss. Finally, we implement our method on top of the meta-embedding framework and conduct experiments on three real-world datasets. The experiments show that our proposed method significantly improves the predictions in the cold-start scenario.