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- ArticleSeptember 2024
Large Language Model Cascades and Persona-Based In-Context Learning for Multilingual Sexism Detection
Experimental IR Meets Multilinguality, Multimodality, and InteractionPages 254–265https://doi.org/10.1007/978-3-031-71736-9_18AbstractThis paper presents an approach for detecting and categorising sexism in social media posts using large language models (LLMs) and ensemble methods. The sEXism Identification in Social neTworks (EXIST) shared task, part of CLEF 2023, consists of ...
- research-articleAugust 2024
A Hierarchical and Disentangling Interest Learning Framework for Unbiased and True News Recommendation
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 3200–3211https://doi.org/10.1145/3637528.3671944In the era of information explosion, news recommender systems are crucial for users to effectively and efficiently discover their interested news. However, most of the existing news recommender systems face two major issues, hampering recommendation ...
- research-articleAugust 2024
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 2072–2081https://doi.org/10.1145/3637528.3671834Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and ...
- research-articleJuly 2024
Trustworthy Recommender Systems
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 15, Issue 4Article No.: 84, Pages 1–20https://doi.org/10.1145/3627826Recommender systems (RSs) aim at helping users to effectively retrieve items of their interests from a large catalogue. For a quite long time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an ...
- research-articleJune 2024
A survey on personalized itinerary recommendation: From optimisation to deep learning
AbstractThe tourism industry is a significant contributor to the global economy, responsible for generating nearly 10% of the world’s GDP and employing around 9% of the global workforce. A crucial aspect of this industry is personalised itinerary ...
Highlights- We discuss deep learning techniques based POI/itinerary recommendations.
- It focuses two research directions: user satisfaction and provider satisfaction.
- It explores user satisfaction into non-personalised and personalised sub-...
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- research-articleNovember 2023
Capacity-aware fair POI recommendation combining transformer neural networks and resource allocation policy▪
AbstractPoint of Interest (POI) recommendations have primarily focused on maximising user satisfaction, while neglecting the needs of POIs and their operators. One such need is recommendation exposure, which can lead to envy among the POIs. Some POIs may ...
Highlights- Propose a capacity-aware fair POI recommendation using deep learning and allocation.
- It captures user interest and ensures POIs get sufficient users to operate services.
- The model simultaneously solves recommendation and fairness ...
- research-articleAugust 2023
Task allocation on networks with execution uncertainty (extended abstract)
IJCAI '23: Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceArticle No.: 729, Pages 6509–6513https://doi.org/10.24963/ijcai.2023/729We study a single task allocation problem where each worker connects to some other workers to form a network and the task requester only connects to some of the workers. The goal is to design an allocation mechanism such that each worker is incentivized ...
- research-articleApril 2023
MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
WWW '23: Proceedings of the ACM Web Conference 2023Pages 1743–1753https://doi.org/10.1145/3543507.3583417State-sponsored trolls are the main actors of influence campaigns on social media and automatic troll detection is important to combat misinformation at scale. Existing troll detection models are developed based on training data for known campaigns (...
- research-articleDecember 2022
Modeling User Demand Evolution for Next-Basket Prediction
- Shoujin Wang,
- Yan Wang,
- Liang Hu,
- Xiuzhen Zhang,
- Qi Zhang,
- Quan Z. Sheng,
- Mehmet A. Orgun,
- Longbing Cao,
- Defu Lian
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 11Pages 11585–11598https://doi.org/10.1109/TKDE.2022.3231018Users’ purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, ...
- ArticleMarch 2023
Maximal Information Propagation with Limited Resources
AbstractWe consider an information propagation game, where the sponsor holds the information and wants to attract more players with a fixed resource. We propose an allocation mechanism to incentivize the existing players to propagate the information to ...
- ArticleNovember 2022
Task Allocation on Networks with Execution Uncertainty
PRIMA 2022: Principles and Practice of Multi-Agent SystemsPages 106–121https://doi.org/10.1007/978-3-031-21203-1_7AbstractWe study a single task allocation problem where each worker connects to some other workers to form a network and the task requester only connects to some of the workers. The goal is to design an allocation mechanism such that each worker is ...
- research-articleNovember 2022
POI recommendation with queuing time and user interest awareness
Data Mining and Knowledge Discovery (DMKD), Volume 36, Issue 6Pages 2379–2409https://doi.org/10.1007/s10618-022-00865-wAbstractPoint-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential ...
- abstractAugust 2022
Data Science and Artificial Intelligence for Responsible Recommendations
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 4904–4905https://doi.org/10.1145/3534678.3542916With the advancement of data science and AI, more and more powerful and accurate recommender systems (RSs) have been developed. They provide recommendation services in various areas, including shopping, eating, travelling and entertainment. RSs have ...
- tutorialJuly 2022
Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 3425–3428https://doi.org/10.1145/3477495.3532685In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. ...
- research-articleApril 2022
Veracity-aware and Event-driven Personalized News Recommendation for Fake News Mitigation
WWW '22: Proceedings of the ACM Web Conference 2022Pages 3673–3684https://doi.org/10.1145/3485447.3512263Despite the tremendous efforts by social media platforms and fact-check services for fake news detection, fake news and misinformation still spread wildly on social media platforms (e.g., Twitter). Consequently, fake news mitigation strategies are ...
- research-articleApril 2022
Efficient itinerary recommendation via personalized POI selection and pruning
Knowledge and Information Systems (KAIS), Volume 64, Issue 4Pages 963–993https://doi.org/10.1007/s10115-021-01648-3AbstractPersonalized itinerary recommendation has garnered wide research interests for their ubiquitous applications. Recommending personalized itineraries is complex because of the large number of points of interest (POI) to consider in order to ...
- research-articleMarch 2022
Estimation of spatial-functional based-line logit model for multivariate longitudinal data
Computational Statistics (CSTAT), Volume 38, Issue 1Pages 79–99https://doi.org/10.1007/s00180-022-01217-4AbstractIn this paper, a novel method is proposed to analyze multivariate longitudinal data that contains spatial location information. The method has the advantage of analyzing the relationship between curves at neighbor time points and observing the ...
- research-articleFebruary 2022
Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data MiningPages 1206–1214https://doi.org/10.1145/3488560.3498457Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selecting top k influential users on social networks as debunkers, but the ...
- surveyJanuary 2022
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
- Tabinda Sarwar,
- Sattar Seifollahi,
- Jeffrey Chan,
- Xiuzhen Zhang,
- Vural Aksakalli,
- Irene Hudson,
- Karin Verspoor,
- Lawrence Cavedon
ACM Computing Surveys (CSUR), Volume 55, Issue 2Article No.: 33, Pages 1–40https://doi.org/10.1145/3490234The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to ...
- ArticleSeptember 2021