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

Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current approaches face three key challenges: (1) The advantages of relational information are obscured by limitations in downstream task designs; (2) Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization; (3) Experience-based construction of corporate relationship graphs lacks effective comparison of different graph structures. To address these limitations, we propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs. Furthermore, we experimentally demonstrate the limitations of existing graph comparison methods based on model downstream task performance. Experimental results across two datasets consistently demonstrate the effectiveness of our proposed task and model. The project is publicly available on GitHub to encourage reproducibility and future research. ( https://github.com/FreddieNIU/NGAT ).

Detecting changes in longitudinal medical imaging using deep learning requires a substantial amount of accurately labeled data. However, labeling these images is notably more costly and time-consuming than labeling other image types, as it requires labeling across various time points, where new lesions can be minor, and subtle changes are easily missed. Deep Active Learning (DAL) has shown promise in minimizing labeling costs by selectively querying the most informative samples, but existing studies have primarily focused on static tasks like classification and segmentation. Consequently, the conventional DAL approach cannot be directly applied to change detection tasks, which involve identifying subtle differences across multiple images. In this study, we propose a novel DAL framework, named Longitudinal Medical Imaging Active Learning (LMI-AL), tailored specifically for longitudinal medical imaging. By pairing and differencing all 2D slices from baseline and follow-up 3D images, LMI-AL iteratively selects the most informative pairs for labeling using DAL, training a deep learning model with minimal manual annotation. Experimental results demonstrate that, with less than 8% of the data labeled, LMI-AL can achieve performance comparable to models trained on fully labeled datasets. We also provide a detailed analysis of the method’s performance, as guidance for future research. The code is publicly available at https://github.com/HelenMa9998/LongitudinalAL.

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.

Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based AL methods, such as entropy and Bayesian, often rely on an aggregate of all pixel-level metrics. However, in imbalanced settings, these methods tend to neglect the significance of target regions, eg., lesions, and tumors. Moreover, uncertainty-based selection introduces redundancy. These factors lead to unsatisfactory performance, and in many cases, even underperform random sampling. To solve this problem, we introduce a novel approach called the Selective Uncertainty-based AL, avoiding the conventional practice of summing up the metrics of all pixels. Through a filtering process, our strategy prioritizes pixels within target areas and those near decision boundaries. This resolves the aforementioned disregard for target areas and redundancy. Our method showed substantial improvements across five different uncertainty-based methods and two distinct datasets, utilizing fewer labeled data to reach the supervised baseline and consistently achieving the highest overall performance. Our code is available at https://github.com/HelenMa9998/Selective_Uncertainty_AL.

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.

Personalised news recommender systems are effective in disseminating news content based on users’ reading histories but can also amplify and proliferate biased media. This work examines the potential of automated sentence rewriting methods, utilising word replacement methods and large language models (LLMs), to mitigate this side effect of recommender systems. We present a two-step workflow: the application of automated sentence rewriting methods to rewrite biased sentences, and the integration of these rewritten sentences into the recommendation process. We evaluate the effectiveness of sentence rewriting approaches in a simulation framework, to assess how well they mitigate the spread of biased news. Our study demonstrates that applying sentence rewriting to users’ reading histories can result in a significant reduction in the propagation of biased media. Our contributions are threefold: we pioneer the use of LLMs for mitigating the spread of biased news by recommender systems; we demonstrate that algorithms trained on debiased content maintain or improve recommendation accuracy; and we provide a comprehensive exploration of the effectiveness of applying sentence rewriting methods to various components within a recommender system, as well as an investigation of the underlying reasons for their efficacy. This work advances our understanding of media bias mitigation in news content and recommendation algorithms, providing valuable insights into how news recommender systems can prevent the dissemination of biased information.

Image emotion classification (IEC) aims to extract the abstract emotions evoked in images. Recently, language-supervised methods such as contrastive language-image pretraining (CLIP) have demonstrated superior performance in image understanding. However, the underexplored task of IEC presents three major challenges: a tremendous training objective gap between pretraining and IEC, shared suboptimal prompts, and invariant prompts for all instances. In this study, we propose a general framework that effectively exploits the language-supervised CLIP method for the IEC task. First, a prompt-tuning method that mimics the pretraining objective of CLIP is introduced, to exploit the rich image and text semantics associated with CLIP. Subsequently, instance-specific prompts are automatically composed, conditioning them on the categories and image content of instances, diversifying the prompts, and thus avoiding suboptimal problems. Evaluations on six widely used affective datasets show that the proposed method significantly outperforms state-of-the-art methods (up to 9.29% accuracy gain on the EmotionROI dataset) on IEC tasks with only a few trained parameters. The code is publicly available at https://github.com/dsn0w/PT-DPC/for research purposes .

\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.

Currently I am at the beginning of my fourth year of a structured PhD programme with an expectation to graduate in May 2024. The advancement of Internet technology has led to the proliferation of accessible online news media, which has overwhelmed people’s lives. Online news platforms have developed personalised recommendation systems to help readers avoid information overload and enhance their experience. However, the filter bubble, one of the side effects of personalised news recommendations, has received severe criticism for limiting readers’ perspectives. Media bias, which is one of the factors causing the “filter bubble” phenomenon, is widely present in news media. It has been extensively studied in the field of social sciences due to its unconscious distortion of readers’ views. Although many studies have focused on examining the effect of media bias on users and their political choices, there is still a lack of direct research on the impact of media bias on news dissemination platforms, such as personalised news recommender systems. My PhD research project aims to explore the influence of media bias on news recommender systems, and understand the factors that accelerate the recommendation of biased news to readers. To help algorithm designers gain insight into the sensitivity of proposed recommendation algorithms to media bias, and to design debiasing algorithms to weaken the impact of media bias on news recommender systems.

Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learning-based methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current state-of-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context.

A recommendation engine that relies solely on interactions between users and items will be limited in its ability to provide accurate, diverse and explanation-rich recommendations. Side information should be taken into account to improve performance. Methods like Factorisation Machines (FM) cast recommendation as a supervised learning problem, where each interaction is viewed as an independent instance with side information encapsulated. Previous studies in top-K recommendation have incorporated knowledge graphs (KG) into the recommender system to provide rich information about the relationships between users, items and entities. Nevertheless, these studies do not explicitly capture the preference of users for the side information. Furthermore, some studies explain the recommendation, but there is no unified method of measuring explanation quality.

Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylised seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalisation of the neural network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/npt-code.

Linyi Yang, Jiazheng Li, Padraig Cunningham, Yue Zhang, Barry Smyth, Ruihai Dong. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Ancient oracle bone inscriptions (OBIs) are important Chinese cultural artefacts, which are difficult and time-consuming to decipher even by the most expert paleographers and, as a result, a large proportion of excavated OBIs remain unidentified. In practice, OBIs are deciphered by translating between different writing systems; Chinese writing systems have evolved over time and ancient OBIs can be deciphered by translating their inscriptions to a known inscription in an adjacent writing system, but this is a complex and time-consuming process. In this paper we propose a novel case-based system, to support this task, allowing a paleographer to present an unknown inscription (image) as a query, to receive a set of similar images from an adjacent writing system with associated scholarly information, and so help guide the deciphering of the query. One important contribution of this work involves the use of an auto-encoder to learn suitable image representations to capture the relationship between two adjacent writing systems. We demonstrate the effectiveness of this approach using a novel, purpose-built case base, and discuss its use in a paleographic setting.

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/

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence.However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner a user's trust in the automated system.Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user's trust in the system.To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech.Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current stateof-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.

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.

User-generated reviews are a plentiful source of user opinions and interests and can play an important role in a range of artificial intelligence contexts, particularly when it comes to recommender systems. In this paper, we describe how natural language processing and opinion mining techniques can be used to automatically mine useful recommendation knowledge from user generated reviews and how this information can be used by recommender systems in a number of classical settings.

To help users discover relevant products and items recommender systems must learn about the likes and dislikes of users and the pros and cons of items. In this paper, we present a novel approach to building rich feature-based user profiles and item descriptions by mining user-generated reviews. We show how this information can be integrated into recommender systems to deliver better recommendations and an improved user experience.

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 opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. Indeed this has become a vital part of the service provided by sites like Amazon and TripAdvisor, so much so that many of us will routinely check appropriate product reviews before making a purchase decision, regardless of whether we intend to purchase online or not. The importance of reviews has highlighted the need to help users to produce better reviews and in this paper we describe the development and evaluation of a Reviewer's Assistant for this purpose. We describe a browser plugin that is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. These suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review. We describe and evaluate a number of different algorithms to identify useful topics to recommend to the user and go on to describe the results of a preliminary live-user trial.

User opinions and reviews are an important part of the modern web and all major e-commerce sites typically provide their users with the ability to provide and access customer reviews across their product catalog. The importance of reviews has driven the need to improve the review quality by providing interactive support for the reviewer and we will demonstrate the first version of an Intelligent Reviewer's Assistant for this purpose. Our browser plugin is designed to work with major sites like Amazon and to provide users with suggestions as they write their reviews. In particular, these suggestions take the form of topics (e.g. product features) that a reviewer may wish to write about and the suggestions automatically adapt as the user writes their review.

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.