In cross-domain recommendation, the cold-start recommendation problem often arises in scenarios where users have interacted with items in a source domain but not in a target domain. A key challenge in this cross-domain recommendation setting is how to effectively transfer user preferences from the source domain to the target domain. Most existing transfer learning models address this challenge but typically require extensive computations and incremental operations, which limit their scalability and efficiency. To overcome these limitations, we propose a novel similarity-based framework, called Similarity-based Transfer Graph Convolution Network (SimTranGCN), designed specifically for cold-start users. Our approach combines item-KNN, deep learning, and graph convolutional models such as LightGCN. SimTranGCN first constructs a similarity matrix across domains, and then uses this matrix to infer user preferences in the target domain based on their interactions in the source domain. Empirical experiments demonstrate that SimTranGCN is highly competitive against existing methods, achieving state-of-the-art performance on two paired domain transfer tasks.

The Form 10-K provides a detailed overview of a company's operations, strategies, competitive environment, and future outlook. Investors meticulously analyse annual changes in these reports to identify subtle shifts in disclosed information. However, financial narratives often contain extensive boilerplate text, which dilutes the efficacy of traditional sentiment analysis. We propose a novel edit-level analysis framework to isolate meaningful changes in Management Discussion and Analysis (MD&A) sections of 10-K filings. Using sentence embeddings, we classify each sentence in the filings for years t and t+1 as Kept (repeated boilerplate), Deleted (removed information), and Added (newly introduced information). We then construct a net sentiment change signal from the difference in average FinBERT-tone sentiment between Added and Deleted content. This Core Sentiment measure has been demonstrated to outperform conventional bag-of-words sentiment measures. Furthermore, based on cleaned and standardised 10-K corpora (e.g., EDGAR-CORPUS) and our Flex10K pre-processing pipeline, our approach provides a reproducible basis for quantifying changes between annual reports.

Cooperative perception technology plays a crucial role in autonomous driving systems by improving safety and enabling real-time decision-making. However, existing LiDAR point cloud processing methods face significant challenges in both local geometric feature extraction and global feature fusion. To address these issues, this paper proposes DF-CoopNet, a cooperative perception framework comprising two core modules: Local Geometry Enhancement (LGE) and Sparse Key Feature Attention (SKFA). The LGE module enhances local geometric representations using a deformable k-nearest neighbor graph structure and adaptive fusion mechanism to effectively detect occluded targets. The SKFA module introduces a hierarchical sparse attention mechanism that balances performance and computational complexity through a Top-k strategy. Extensive experiments on the OPV2V and V2V4Real datasets demonstrate that DF-CoopNet significantly outperforms existing methods while maintaining robust detection performance even with substantially reduced point cloud data, validating its effectiveness for real-world cooperative perception applications.

The performance of Active Learning (AL) methods varies widely, influenced by the query strategy, model, and dataset, with the reasons for variation in performance still unclear and insufficiently studied. However, commonly used metrics like accuracy, precision, and recall provide only limited analytical perspectives. No research has effectively uncovered or explained the reasons behind these performance variations, leaving a gap in understanding of the factors that influence the success or failure of AL methods. To address this issue, we propose a novel method and tool leveraging Voronoi Diagrams to visualize AL processes by illustrating interactions between classification decision boundary changes and queried samples across AL iterations. We perform experiments on synthetic and real-world datasets to validate the effectiveness of our method and analyze various AL query strategies. By visualizing the AL process, we illustrate how different query strategies progressively select samples and influence performance in each iteration. This reveals the potential benefits of adapting query strategies at different learning stages to improve active learning efficiency.

10-K filings represent a significant repository for financial text analysis, encompassing both standardized quantitative indicators and rich unstructured text content. In recent years, the efficacy of rule-based extraction methods has been progressively limited due to changes in the 10-K filing format. In this study, we propose a novel layout-robust segmentation approach that achieves identification of financial report by combining fuzzy matching and structural heuristics. Our approach has been employed in recent 10-K filings (2021-2024), resulting in a standardized dataset with item-level segmentation. Furthermore, an automated validation protocol was developed in order to assess coverage and ranking consistency. Analysis of the protocol indicates that our approach achieves an average extraction accuracy of 87.8%. Finally, a case study utilising Item 1A to forecast short-term stock volatility provides a practical demonstration of the application of the corpus. This case study not only serves to validate the corpus but also showcases its compatibility with EDGAR-CORPUS. Code, benchmarks, segmented 10-K filings, and case studies are publicly available on GitHub. Our GitHub repository: https://github.com/johnny-xiao-li/Flex_10K

News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack of explanation for these recommendations can lead to mistrust among users and lack of acceptance of recommendations. To address this issue, we propose a new explainable news model to construct a topic-aware explainable recommendation approach that can both accurately identify relevant articles and explain why they have been recommended, using information from associated topics. Additionally, our model incorporates two coherence metrics applied to assess topic quality, providing a measure of the interpretability of these explanations. The results of our experiments on the MIND (MIcrosoft News Dataset) indicate that the proposed explainable NRS outperforms several other baseline systems, while it is also capable of producing interpretable topics measured by coherence metrics. Furthermore, we present a case study through real-world examples showcasing the usefulness of our NRS for generating explanations.

News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability to news recommendation systems remains to be validated. This paper introduces RecPrompt, the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. This framework incorporates a news recommender and a prompt optimizer that applies an iterative bootstrapping process to enhance recommendations through automatic prompt engineering. Extensive experimental results with 400 users show that RecPrompt can achieve an improvement of 3.36% in AUC, 10.49% in MRR, 9.64% in nDCG@5, and 6.20% in nDCG@10 compared to deep neural models. Additionally, we introduce TopicScore, a novel metric to assess explainability by evaluating LLM's ability to summarize topics of interest for users. The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.

This workshop aims to explore the evaluation and application of Large Language Models (LLMs) in recommendation systems (RSs), highlighting innovations, challenges, and future directions, focusing on enhancing RSs through LLM techniques such as prompting, fine-tuning, and developing conversational systems. By gathering researchers and partitioners from both academia and industry, the workshop focuses on discussing state-of-the-art techniques and addressing challenges and innovative applications in various sectors. At last, the workshop encourages research on topics including LLM integration, evaluating LLM-based RSs, transparency, and conversational RS development, aiming to set a research agenda for future RS advancements.

\beginabstract Recently, modelling heterogeneous interactions in recommender systems has attracted research interest. Real-world scenarios involve sequential multi-type user-item interactions such as ''shape view'', ''shape add-to-favourites'', ''shape add-to-cart'' and ''shape purchase''. Graph Neural Network (GNN) methods have been widely adopted in Representation Learning of similar sequential user-item interactions. Promising results have been achieved by the integration of GNNs and transformers for self-attention. However, GNN based methods suffer from limited capability in handling global user-item interaction dependencies, particularly for long sequences. Moreover, these models require high computational cost of transformers, due to the quadratic memory and time complexity with respect to sequence length. This results in memory bottlenecks and slow training especially in computational resource-constrained environments. To address these challenges, we propose the FATH model which employs Flash Attention mechanism to reduce the high-bandwidth memory usage over higher-order user-item interaction sequences. Experimental results show that our model improves the training speed and reduces the memory usage with better recommendation performance in comparison with the state-of the art baselines.

Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using advanced natural language processing techniques to extract semantic information from rich textual data, employing content-based methods derived from local historical news. However, this approach lacks a global perspective, failing to account for users’ hidden motivations and behaviors beyond semantic information. To address this challenge, we propose a novel model called GLORY (Global-LOcal news Recommendation sYstem), which combines global representations learned from other users with local representations to enhance personalized recommendation systems. We accomplish this by constructing a Global-aware Historical News Encoder, which includes a global news graph and employs gated graph neural networks to enrich news representations, thereby fusing historical news representations by a historical news aggregator. Similarly, we extend this approach to a Global Candidate News Encoder, utilizing a global entity graph and a candidate news aggregator to enhance candidate news representation. Evaluation results on two public news datasets demonstrate that our method outperforms existing approaches. Furthermore, our model offers more diverse recommendations1.

In the area of natural language processing, various financial datasets have informed recent research and analysis including financial news, financial reports, social media, and audio data from earnings calls. We introduce a new, large-scale multi-modal, text-audio paired, earnings-call dataset named MAEC, based on S&P 1500 companies. We describe the main features of MAEC, how it was collected and assembled, paying particular attention to the text-audio alignment process used. We present the approach used in this work as providing a suitable framework for processing similar forms of data in the future. The resulting dataset is more than six times larger than those currently available to the research community and we discuss its potential in terms of current and future research challenges and opportunities. All resources of this work are available at https://github.com/Earnings-Call-Dataset/

The volatility forecasting task refers to predicting the amount of variability in the price of a financial asset over a certain period. It is an important mechanism for evaluating the risk associated with an asset and, as such, is of significant theoretical and practical importance in financial analysis. While classical approaches have framed this task as a time-series prediction one – using historical pricing as a guide to future risk forecasting – recent advances in natural language processing have seen researchers turn to complementary sources of data, such as analyst reports, social media, and even the audio data from earnings calls. This paper proposes a novel hierarchical, transformer, multi-task architecture designed to harness the text and audio data from quarterly earnings conference calls to predict future price volatility in the short and long term. This includes a comprehensive comparison to a variety of baselines, which demonstrates very significant improvements in prediction accuracy, in the range 17% - 49% compared to the current state-of-the-art. In addition, we describe the results of an ablation study to evaluate the relative contributions of each component of our approach and the relative contributions of text and audio data with respect to prediction accuracy.

We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world datasets to demonstrate improved rating prediction performance, compared to state-of-the-art alternatives, while producing effective, personalized explanations.

Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used to represent users and items, which can lead to a lack of recommendation transparency and explainability. User-generated, customer reviews are now commonplace on many websites, providing users with an opportunity to convey their experiences and opinions of products and services. As such, these reviews have the potential to serve as a useful source of recommendation data, through capturing valuable sentiment information about particular product features. In this paper, we present a novel deep learning recommendation model, which co-learns user and item information from ratings and customer reviews, by optimizing matrix factorization and an attention-based GRU network. Using real-world datasets we show a significant improvement in recommendation performance, compared to a variety of alternatives. Furthermore, the approach is useful when it comes to assigning intuitive meanings to latent features to improve the transparency and explainability of recommender systems.

This paper describes a novel approach to product recommendation that is based on opinionated product descriptions that are automatically mined from user-generated product reviews. We present a recommendation ranking strategy that combines similarity and sentiment to suggest products that are similar but superior to a query product according to the opinion of reviewers. We demonstrate the benefits of this approach across a variety of Amazon product domains.

User generated reviews are now a familiar and valuable part of most e-commerce sites since high quality reviews are known to influence purchasing decisions. In this demonstration we describe work on the Reviewer's Assistant (RA), which is a recommendation system that is designed to help users to write better quality reviews. It does this by suggesting relevant topics that they may wish to discuss based on the product they are reviewing and the content of their review so far.