In 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 ...

Social 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 ...

The 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 ...

A 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 ...

Mobile 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 ...

Modern 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 ...

The 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 ...

Online 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 ...

How 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 ...

Recent 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 ...

Social 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, ...

User 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 ...

In 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 ...

Social 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. ...

The 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 ...

User 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 ...

Targeted 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 ...

In 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 ...

Complex 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 ...

In 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.