Sequential recommendation predicts the next item based on user behavior patterns. However, due to the influence of unobserved confounders, there are often significant differences between the distributions of training and testing data, resulting in Out-Of-Distribution (OOD) problems that severely degrade recommendation performance. Existing causality-based sequential recommendation methods typically model the unobserved confounders as confounders and assume that they are fully observable, and use causal intervention to remove confounding. However, in reality, the unobserved confounders is complex and unobservable. In this case, the model may learn spurious correlations, which impair its generalization ability to OOD data. To address this, we propose Causal Multi-Interest Fusion for Distribution-Robust Sequential Recommendation (CURE). Specifically, we use stable long-term and periodic interests, unaffected by confounders, as mediators, and apply front-door adjustment to remove the influence of unobserved confounders. Based on front-door adjustment theory, we derive the formula for the target causal effect and employ a multi-interest modeling method to obtain comprehensive representations for each mediator, fully uncovering the causal pathways. Furthermore, we design four modules to implement the key components of the target causal effect formula. During model optimization, to address the lack of explicit interest labels, we leverage large language models (LLMs) to generate fine-grained interest labels, providing high-quality supervision signals for model training. Experiments conducted across four real-world datasets demonstrate that CURE effectively alleviates OOD issues and significantly improves sequential recommendation performance. We will release the code once accepted.

In information security, random number quality is closely related to cryptographic system security; moreover, random number quality depends on the corresponding entropy source quality. Therefore, evaluating the entropy source quality is extremely important. For existing evaluation methods, the ability of statistical-based entropy estimators to extract and learn data information is weak, resulting in lower entropy evaluation accuracies for some complex entropy sources. The prediction-based (especially neural network-based) entropy estimators with machine learning techniques have strong data-fitting and feature-extracting capabilities and can more accurately estimate the entropy values of complex entropy sources. However, owing to the relatively simple architecture of 1D neural networks, the 1D neural networks used by these estimators frequently reach bottlenecks, seriously limiting the further improvement in entropy estimation accuracy. Considering the above issues, this paper innovatively proposes an entropy estimation method based on a 2D-CNN and a multi-head self-attention mechanism. First, we built the MA-DG Net model. This model converts 1D random number sequences into 2D images via the GAF and DFT methods and then uses a 2D-CNN to extract and learn feature information from 2D images while retaining the original 1D sequential feature information via a multi-head self-attention mechanism. Next, we train the model to find its optimal parameters. Finally, we test the evaluation effect of the model using simulated datasets with known min-entropy and a real-world dataset with unknown min-entropy. The results show that compared with the entropy estimators in the experiment, our model achieves the lowest average relative error in entropy estimation on the simulated dataset of only 1.03%. In the real-world dataset, our model achieves the lowest entropy estimation value, which is an average of 0.88 lower than that of the other entropy estimators in the experiment.