Abstract Recommendation systems often contain both rich relational structures and diverse multimodal information. The multiple relations among users, items, and auxiliary entities naturally form a heterogeneous information network. A central challenge in developing scalable recommendation systems in the era of big data is efficiently identifying similar users and items across hop- n relational paths in such networks. Hashing has been widely adopted for dimensionality and data size reduction; however, existing techniques are primarily designed for directly connected (i.e., hop-1) features and rarely exploit higher-order relational information. To address this limitation, we propose two methods. First, we develop relation-aware hashing that extends locality-sensitive hashing to encode hop- n metapath semantics and builds metapath-specific hash blocks as a scalable recall layer for candidate generation. Second, we introduce a multimodal learning-to-hash model that learns binary codes from fused text, image, and temporal features, and aligns Hamming-space neighbourhoods with metapath-guided user neighbourhood graphs. By jointly leveraging both relation-aware encoding and multimodal content, the proposed approaches enable efficient neighbourhood construction and recommendation in large-scale heterogeneous networks. Extensive experiments on three real-world datasets show that our framework achieves substantial efficiency gains while delivering competitive recommendation accuracy compared with baselines.

Radiologists have an important task of diagnosing thyroid nodules present in ultra sound images. Although reporting systems exist to aid in the diagnosis process, these systems do not provide explanations about the diagnosis results. We present ThyExp -- a web based toolkit for it use by medical professionals, allowing for accurate diagnosis with explanations of thyroid nodules present in ultrasound images utilising artificial intelligence models. The proposed web-based toolkit can be easily incorporated into current medical workflows, and allows medical professionals to have the confidence of a highly accurate machine learning model with explanations to provide supplementary diagnosis data. The solution provides classification results with their probability accuracy, as well as the explanations in the form of presenting the key features or characteristics that contribute to the classification results. The experiments conducted on a real-world UK NHS hospital patient dataset demonstrate the effectiveness of the proposed approach. This toolkit can improve the trust of medical professional to understand the confidence of the model in its predictions. This toolkit can improve the trust of medical professionals in understanding the models reasoning behind its predictions.

Recommender systems contain rich relation information. The multiple relations in a recommender system form a heterogeneous information network. How to efficiently find similar users and items based on hop-n relations in heterogeneous information networks is one significant challenge to develop scalable recommender systems in the era of big data. Hashing has been popularly used for dimensionality reduction and data size reduction. Current hashing techniques mainly focus on hashing for directly related (i.e. hop-1) features. This paper proposes to develop relation-aware hashing techniques to bridge this gap. The proposed approaches use locality sensitive hashing (LSH) and consider hop-n relations in an information network to construct user or item blocks. They help facilitate efficient neighborhood formation and recommendation making. The experiments conducted on a large-scale real-life dataset show that the proposed approaches are effective.

Health claims are sentences on the food product packages to claim the nutrition and the benefits of the nutrition. Consumers in different European contexts often have difficulties understanding health claims, leading to increased confusion about and decreased trust in the food they buy. Focusing on this problem, we develop a toolkit for improving the communication of health claims for consumers. The toolkit provides (1) interactive activities to disseminate knowledge about health claims to the public, and (2) an NLP-based analysis and prediction engine that food manufacturers can use to estimate how consumers like the health claims that the manufacturers created. By using the AI-powered toolkit, consumers, manufacturers, and food safety regulators are engaged in determining the different linguistic and cultural barriers to the effective communication of health claims and formulating solutions that can be implemented on multiple levels, including regulation, enforcement, marketing, and consumer education.