Harnessing Business Insights from Unstructured Customer Data. Resulting from customers’ widespread uptake of online channels to buy and communicate has been a surge in online reviews and social media posts. This textual information offers a viable alternative to surveys that Australian businesses currently conduct to obtain customer insights. However, these reviews are unstructured and require substantial pre-processing to extract underlying customer perceptions. Therefore, this project aims to ....Harnessing Business Insights from Unstructured Customer Data. Resulting from customers’ widespread uptake of online channels to buy and communicate has been a surge in online reviews and social media posts. This textual information offers a viable alternative to surveys that Australian businesses currently conduct to obtain customer insights. However, these reviews are unstructured and require substantial pre-processing to extract underlying customer perceptions. Therefore, this project aims to develop a novel machine learning approach to quantify the business-relevant information contained in textual information shared by customers online. This alternative approach will provide significant cost-saving benefits for a range of Australian companies, such as retailers, hotels, airlines and restaurants.Read moreRead less
Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. ....Privacy-Aware and Personalised Explanation Overlays for Recommender Systems. AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.Read moreRead less