Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/item IDs, which are unavailable in most real-world scenarios. Consequently, many efforts have focused on learning disentangled representations through multi-domain joint training to bridge the domain gaps. Recent Large Language Model (LLM)-based approaches show promise, they still face critical challenges, including: (1) the \textbf{item ID tokenization dilemma}, which leads to vocabulary explosion and fails to capture high-order collaborative knowledge; and (2) \textbf{insufficient domain-specific modeling} for the complex evolution of user interests and item semantics. To address these limitations, we propose \textbf{GenCDR}, a novel \textbf{Gen}erative \textbf{C}ross-\textbf{D}omain \textbf{R}ecommendation framework. GenCDR first employs a \textbf{Domain-adaptive Tokenization} module, which generates disentangled semantic IDs for items by dynamically routing between a universal encoder and domain-specific adapters. Symmetrically, a \textbf{Cross-domain Autoregressive Recommendation} module models user preferences by fusing universal and domain-specific interests. Finally, a \textbf{Domain-aware Prefix-tree} enables efficient and accurate generation. Extensive experiments on multiple real-world datasets demonstrate that GenCDR significantly outperforms state-of-the-art baselines. Our code is available in the supplementary materials.

In the digital era, traditional methods of historical learning are undergoing a transition, as there is a growing demand for intuitive and visually engaging approaches. Vi-sualization, particularly timeline-based visualization, is critical in bridging the gap between extensive and complex historical events and history learners due to its intuitive features. Despite the advances in existing timeline visualization tools, they often face challenges: visualization creators spend much time in the materials preparation stage, especially for designers unfamiliar with the history; the lack of flexible interaction limits users from designing customized representations, which decreases the user's interest. To address these issues, we have developed Create3DHistory, a system that leverages the advanced Artificial Intelligence Generated Content and visualization technologies, enabling users to easily generate historical events content and create 3D and 2D historical event timeline representation. Users only need to input the name of a historical event, and the system will automatically generate corresponding content and deploy it onto the timeline representation. Moreover, users can edit the generated content or upload self-prepared multimedia materials for customized event visualization. The user study has demonstrated that Create3DHistory can effectively assist users in customizing personalized historical event timelines. Overall, the proposed tool simplifies the timeline creation process using AIGC technology, providing an overview and detailed information on-demand functions for various tasks.

Modern recommender systems face a critical challenge in complying with privacy regulations like the “right to be forgotten“: removing a user's data without disrupting recommendations for others. Traditional unlearning methods address this by partial model updates, but introduce propagation bias-where unlearning one user's data distorts recommendations for behaviorally similar users, degrading system accuracy. While retraining eliminates bias, it is computationally prohibitive for large-scale systems. To address this challenge, we propose CRAGRU, a novel framework leveraging Retrieval-Augmented Generation (RAG) for efficient, user-specific unlearning that mitigates bias while preserving recommendation quality. CRAGRU decouples unlearning into distinct retrieval and generation stages. In retrieval, we employ three tailored strategies designed to precisely isolate the target user's data influence, minimizing collateral impact on unrelated users and enhancing unlearning efficiency. Subsequently, the generation stage utilizes an LLM, augmented with user profiles integrated into prompts, to reconstruct accurate and personalized recommendations without needing to retrain the entire base model. Experiments on three public datasets demonstrate that CRAGRU effectively unlearns targeted user data, significantly mitigating unlearning bias by preventing adverse impacts on non-target users, while maintaining recommendation performance comparable to fully trained original models. Our work highlights the promise of RAG-based architectures for building robust and privacy-preserving recommender systems. The source code is available at: https://github.com/zhanghaichao520/LLM_rec_unlearning.

The stock market is a complex, nonlinear, and sentiment-driven system where price movements are influenced not only by quantitative indicators but also by market sentiment. While existing approaches—ranging from traditional time series models to deep learning—have made progress in modeling financial data, they often neglect the affective dimensions that drive investor behavior. To address this limitation, we propose a hybrid stock prediction model based on Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM), which captures both short-term fluctuations and long-term dependencies in time series data. Furthermore, we integrate sentiment features derived from financial news using natural language processing techniques, allowing the model to incorporate qualitative insights alongside numerical data. Experimental results across multiple stocks show that our model outperforms existing methods in terms of accuracy and robustness. This study demonstrates the value of combining affective computing with deep temporal modeling for more reliable and socially aware financial forecasting.