Francesco Ricci, Professor
Free University of Bozen-Bolzano, Italy
Title:
Computing Effective Recommendations for Clusters of Tourists
Abstract:
Recommender systems have been introduced as information search and filtering tools for providing suggestions for items to be of use to a user. State of the art recommender systems mostly focus on the usage of data mining and information retrieval techniques to predict to what extent an item fits user needs and wants. But often they end up in making uninteresting suggestions especially in complex domains, such as tourism. In this talk, classical recommender systems ideas will be introduced and critically scrutinised in the attempt to better understand the role of observed and predicted choices and preferences. We will discuss some of the key ingredients necessary to build a useful recommender system. Hence, we will point out some limitations and open challenges for recommender systems research. We will also present a novel recommendation technique that leverages data collected from observation of tourists behaviour to generate more useful individual and recommendations that are adapted to a cluster of tourists with similar behaviour.
Bio:
Francesco Ricci is full professor at the Faculty of Computer Science, Free University of Bozen-Bolzano. F. Ricci has established in Bolzano a reference point for the research on Recommender Systems. He has co-edited the Recommender Systems Handbook (Springer 2015), and has been actively working in this community as President of the Steering Committee of the ACM conference on Recommender Systems (2007-2010). He was previously (from 2000 to 2006) senior researcher and the technical director of the eCommerce and Tourism Research Lab (eCTRL) at ITC-irst (Trento, Italy). F. Ricci’s research interests cover: machine learning, user modelling, recommender systems and ICT applications to travel and tourism. Francesco Ricci is author of approximately two hundred refereed publications and, according to Google Scholar, has H-index 57 and around 20,000 citations.
Jie Zhang, Associate Professor
School of Computer Science and Engineering
Nanyang Technological University, Singapore
Title:
Deep Learning-Based Recommendation Models with Side Information
Abstract:
Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This talk covers different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). It also includes a brief overview on some of the recent deep learning-based recommendation models with side information. Finally, remaining challenges and new potential directions will also be discussed.
Bio:
Jie Zhang is currently an Associate Professor of the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He obtained Ph.D. in Cheriton School of Computer Science from University of Waterloo, Canada, and was the recipient of the Alumni Gold Medal at the 2009 Convocation Ceremony. The Gold Medal is awarded once a year to honour the top PhD graduate from the University of Waterloo. Jie Zhang’s research is in the general area of Artificial Intelligence and focuses on trust modeling and preference modeling for various emerging application domains (e.g. e-commerce, VANET, IoT, collaborative systems, etc.). His papers have been published by top AI conferences (such as NeurIPS, AAAI and IJCAI) and top journals (such as TKDE and TWEB). He has won several best paper awards at the conferences like IM, CNSM, IFIPTM, etc. Jie Zhang is also active in serving research communities. He is serving as Senior Editor of the Electronic Commerce Research and Applications journal and Associate Editor of IEEE TNSM. He also served as General Chairs and PC Chairs for several international conferences. He will serve as the General Chair for RecSys’23.
Shlomo Berkovsky, Associate Professor and Computer Scientist
Australian Institute of Health Innovation, Australian
Title:
Health Personalisation: From Wellbeing to Medicine
Abstract:
Current agenda of health personalisation and recommendation research mainly revolves around lifestyle and wellbeing. A number of works on personalised technologies for physical activity, food intake, mental support, health information consumption, and more have been presented at RecSys and related conferences. While these mainly addressed the patient as the recipient of the personalised service, strikingly little attention has been paid to personalised medical applications targeting clinical users. In this talk, we turn the spotlight to such medical use cases and the advantages personalisation can bring there. We will overview the established health care processes and highlight the touch points, where personalised support can improve the clinician’s decision making. Also, we will discuss the differences between patient- and clinician-facing personalisation, particularly focussing on transparency and explainability.
Bio:
Shlomo Berkovsky is a Computer Scientist, with theoretical and applied expertise in several areas related to human-centric applications of artificial intelligence. He currently leads the Precision Health stream of the Australian Institute of Health Innovation, which focusses on the application of machine learning methods to develop patient models and personalised predictions of diagnosis and care. He also studies how sensors and physiological responses can predict medical conditions, and how clinicians and patients interact with health technologies.
Lina Yao, Associate Professor
University of New South Wales (UNSW), Australia
Title:
Adversarial Learning in Deep Learning based Recommender Systems
Abstract:
Deep neural networks have been demonstrating its effectiveness in recommender systems research. Recently, adversarial learning have garnered increasing interest and been leading to a surging enthusiasm for applying adversarial learning to improve recommendation performance from different aspects, including raising model robustness, alleviating data sparsity, generating initial profiles for cold-start users or items. In this tutorial, I will briefly introduce our recent research progress on how the adversarial learning is leveraged to alleviate multiple challenges of deep learning based recommender systems, in terms of dealing with sparse and missing data, and data noise (passive and active noise) for robustness of recommender systems.
Bio:
Dr. Lina Yao is currently a Scientia Associate Professor at University of New South Wales (UNSW), Australia. Her research lies in data mining and machine learning with focus on recommender systems, activity recognition, Brain Computer Interface and Internet of Things. She has published over 150 peer-reviewed papers in prestigious journals and top international conferences in the areas of data mining, machine learning and intelligent systems including ACM CACM, ACM CUSR, IEEE TMC, ACM TIST, IEEE TKDE, ACM TKDD, ACM TOIT, PR, IEEE TNSRE, IEEE TNNLS, IEEE CYB, IEEE IIT, JBHI, NeurIPS, SIGKDD, ICDM, UbiComp, AAAI, SIGIR, IJCAI and CIKM. She is serving as the Associate Editor for ACM Transactions on Sensor Networks (TOSN).
Tong Chen, Research Fellow
University of Queensland, Australia
Title:
Substitute Recommendation: Addressing Personalization and Interpretability
Abstract:
As a fundamental yet significant process in personalized recommendation, candidate generation and suggestion effectively help users spot the most suitable items for them. Consequently, identifying substitutable items that are interchangeable opens up new opportunities to refine the quality of generated candidates. However, in the emerging research on substitute recommendation, existing methods merely treat this problem as mining pairwise item relationships without the consideration of users’ personal preferences. Moreover, the substitutable relationships are implicitly identified through the learned latent representations of items, which leads to uninterpretable recommendation results. In this talk, we will first enumerate, review, and analyze contemporary solutions to substitute recommendation. Then, after identifying the shortcomings of off-the-shelf substitute recommenders, we further present a novel attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives.
Bio:
Tong Chen received his PhD degree in Computer Science from The University of Queensland in 2020. He is currently a postdoctoral research fellow with the Data Science research group, School of Information Technology and Electrical Engineering, The University of Queensland. His research work has been published on top venues like SIGIR, SIGKDD, ICDE, WWW, ICDM, IJCAI, AAAI, CIKM, TOIS, TKDE, etc., where his research interests include data mining, recommender systems, user behavior modelling and predictive analytics.