Sequence-based Point-Of-Interest (POI) recommendations are increasingly crucial for location-based services and social platforms, offering nuanced insights into predicting user preferences from historical interaction patterns. However, a significant challenge arises from the non-uniform distribution of user-POI interaction sequences, where user preferences are often obscured by irregular and sporadic activities. This paper proposes an innovative Uniform Sequence Balancing (USB) strategy, addressing the critical issue of non-uniform sequences by utilizing the standard deviation of time intervals to achieve uniformity. Our approach transforms non-uniform sequences into uniform ones, thereby facilitating more accurate preference capture. We leverage the Transformer eXtra Long (Transformer-XL) model, known for its ability to discern long-term dependencies, and integrate it with our USB strategy to propose the Sequential Transformer-XL Recommender (STR). Our comprehensive experiments on two widely used public datasets demonstrate the effectiveness of STR, which significantly outperforms state-of-the-art models. The proposed STR not only optimizes recommendation performance but also paves the way for future research on sequence-based recommendation systems.

In the digital era, social media platforms play a crucial role in forming user communities, yet the challenge of protecting user privacy remains paramount. This paper proposes a novel framework for identifying and analyzing user communities within social media networks, emphasizing privacy protection. In detail, we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users, thereby maintaining confidentiality. Finally, we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches, demonstrating its effectiveness in community detection while upholding stringent privacy standards. This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.

Event extraction is an important part of natural language information extraction, and it’s widely employed in other natural language processing tasks including question answering and machine reading comprehension. However, there is a lack of recent comprehensive survey papers on event extraction. In the past few years, numerous high-quality and innovative event extraction methods have been proposed, making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future studies. In addition, event detection is a fundamental sub-task in event extraction, previous survey papers have often overlooked the related work on event detection. Therefore, this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction, including recent advancements and an analysis of previous research on event detection. The resources for event extraction are first introduced in this research, and then the numerous neural network models currently employed in event extraction tasks are divided into four types: word sequence-based methods, graph-based neural network methods, external knowledge-based approaches, and prompt-based approaches. We compare and contrast them in depth, pointing out the flaws and difficulties with existing research. Finally, we discuss the future of event extraction development.