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- short-paperMay 2024
"All of Me": Mining Users' Attributes from their Public Spotify Playlists
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 963–966https://doi.org/10.1145/3589335.3651459In the age of digital music streaming, playlists on platforms like Spotify have become an integral part of individuals' musical experiences. People create and publicly share their own playlists to express their musical tastes, promote the discovery of ...
- research-articleMarch 2024
Emoji are Effective Predictors of User’s Demographics
ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningPages 784–792https://doi.org/10.1145/3625007.3629129Social media platforms like Twitter provide rich data that can offer insights into various aspects of users' behavior. In this study, we explore the potential of emoji usage as a means for demographic prediction. Leveraging a Twitter dataset of 18,689 ...
- tutorialOctober 2023
Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open Challenges
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementPages 5216–5219https://doi.org/10.1145/3583780.3615292The proposed tutorial aims to familiarise the CIKM community with modern user profiling techniques that utilise Graph Neural Networks (GNNs). Initially, we will delve into the foundational principles of user profiling and GNNs, accompanied by an overview ...
- research-articleAugust 2023
Profiling of Conceptual Systems Based on a Complex of Methods of Psychosemantics and Machine Learning
Automatic Documentation and Mathematical Linguistics (SPADML), Volume 57, Issue 4Pages 193–205https://doi.org/10.3103/S0005105523040027AbstractA new approach to profiling users of social internet services and the concept of recommender systems based on this approach are presented. The proposed approach is based on the integration of the methods and models of machine learning with the ...
- research-articleJuly 2023
You Are How You Use Apps: User Profiling Based on Spatiotemporal App Usage Behavior
ACM Transactions on Intelligent Systems and Technology (TIST), Volume 14, Issue 4Article No.: 71, Pages 1–21https://doi.org/10.1145/3597212Mobile apps have become an indispensable part of people’s daily lives. Users determine what apps to use and when and where to use them based on their tastes, interests, and personal demands, depending on their personality traits. This article aims to ...
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- short-paperJuly 2023
FairUP: A Framework for Fairness Analysis of Graph Neural Network-Based User Profiling Models
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalPages 3165–3169https://doi.org/10.1145/3539618.3591814Modern user profiling approaches capture different forms of interactions with the data, from user-item to user-user relationships. Graph Neural Networks (GNNs) have become a natural way to model these behaviours and build efficient and effective user ...
- research-articleJune 2023
Intelligent Mobile User Profiling for Maximum Performance
Applied Computer Systems (ACSS), Volume 28, Issue 1Pages 148–155https://doi.org/10.2478/acss-2023-0014AbstractThe use of smartphones and their applications is expanding rapidly, thereby increasing the demand of computational power and other hardware resources of the smartphones. On the other hand, these small devices can have limited resources of ...
- research-articleJune 2023
URLytics: Profiling Forum Users from Their Posted URLs
ASONAM '22: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningPages 510–513https://doi.org/10.1109/ASONAM55673.2022.10068682Online forums contain a substantial amount of data, but very few studies have focused on mining the URLs posted by users. How can we fully leverage these posted URLs to extract as much information as possible about forum users? We perform a systematic ...
- research-articleOctober 2022
Sequential Banking Products Recommendation and User Profiling in One Go
ICAIF '22: Proceedings of the Third ACM International Conference on AI in FinancePages 317–324https://doi.org/10.1145/3533271.3561697How can banks recommend relevant banking products such as debit, credit cards or term deposits, as well as learn a rich user representation for segmentation and user profiling, all via a single model? We present a sequence-to-item recommendation ...
- short-paperOctober 2022
Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact and Mistreatment in Behavioural User Profiling
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 4399–4403https://doi.org/10.1145/3511808.3557584Recent approaches to behavioural user profiling employ Graph Neural Networks (GNNs) to turn users' interactions with a platform into actionable knowledge. The effectiveness of an approach is usually assessed with accuracy-based perspectives, where the ...
- research-articleOctober 2022
Personality-Driven Social Multimedia Content Recommendation
MM '22: Proceedings of the 30th ACM International Conference on MultimediaPages 7290–7299https://doi.org/10.1145/3503161.3548769Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, ...
- research-articleOctober 2022
Joint user profiling with hierarchical attention networks
Frontiers of Computer Science: Selected Publications from Chinese Universities (FCS), Volume 17, Issue 3https://doi.org/10.1007/s11704-022-1437-6AbstractUser profiling by inferring user personality traits, such as age and gender, plays an increasingly important role in many real-world applications. Most existing methods for user profiling either use only one type of data or ignore handling the ...
- research-articleAugust 2022
Mixture of Virtual-Kernel Experts for Multi-Objective User Profile Modeling
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 4257–4267https://doi.org/10.1145/3534678.3539062In industrial applications like online advertising and recommendation systems, diverse and accurate user profiles can greatly help improve personalization. Deep learning is widely applied to mine expressive tags to users from their historical ...
- research-articleJuly 2022
Social Media Profiling Continues to Partake in the Development of Formalistic Self-Concepts. Social Media Users Think So, Too.
AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and SocietyPages 238–252https://doi.org/10.1145/3514094.3534192Social media platforms generate user profiles to recommend informational resources including targeted advertisements. The technical possibilities of user profiling methods go beyond the classification of individuals into types of potential customers. ...
- research-articleJanuary 2022
Fake News detection using n-grams for PAN@CLEF competition
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 42, Issue 5Pages 4633–4640https://doi.org/10.3233/JIFS-219251The paper presents a classifier for fake news spreaders detection in social media. Detecting fake news spreaders is an important task because this kind of disinformation aims to change the reader’s opinion about a relevant topic for the society. This work ...
- research-articleJanuary 2022
User interests profiling using fuzzy regression tree
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Volume 41, Issue 3Pages 191–203https://doi.org/10.1504/ijahuc.2022.126114User modelling is an essential process for recommender systems. In user interests modelling, two kinds of approaches can be found. The first uses the text extracted from the user's browsing history to predict his interest degree, while the second, besides ...
- research-articleDecember 2021
User profiling by network observers
- Roberto Gonzalez,
- Claudio Soriente,
- Juan Miguel Carrascosa,
- Alberto Garcia-Duran,
- Costas Iordanou,
- Mathias Niepert
CoNEXT '21: Proceedings of the 17th International Conference on emerging Networking EXperiments and TechnologiesPages 212–222https://doi.org/10.1145/3485983.3494859Targeted online advertising is a multi-billion dollar business based on the ability of profiling and delivering targeted ads to a wide range of users. Due to the privacy erosion associated with such business, researchers are trying to understand how ...
- research-articleNovember 2021
Profiling Users for Question Answering Communities via Flow-Based Constrained Co-Embedding Model
ACM Transactions on Information Systems (TOIS), Volume 40, Issue 2Article No.: 34, Pages 1–38https://doi.org/10.1145/3470565In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as atomic units, leading to the mismatch between them; they are ...
- research-articleNovember 2021
Dynamic Structural Role Node Embedding for User Modeling in Evolving Networks
ACM Transactions on Information Systems (TOIS), Volume 40, Issue 3Article No.: 46, Pages 1–21https://doi.org/10.1145/3472955Complex user behavior, especially in settings such as social media, can be organized as time-evolving networks. Through network embedding, we can extract general-purpose vector representations of these dynamic networks which allow us to analyze them ...
- research-articleNovember 2021
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
Marketing Science (MKTGS), Volume 40, Issue 6Pages 1059–1080https://doi.org/10.1287/mksc.2021.1293In recent years, there has been significant interest in understanding users’ online content consumption patterns. But the unstructured, high-dimensional, and dynamic nature of such data makes extracting valuable insights challenging. Here we propose a ...
We propose an interpretable model that combines the simplicity of matrix factorization with the flexibility of neural networks to model evolving user interests by efficiently extracting nonlinear patterns from massive text data collections.