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 of relevant literature. We will subsequently systematically examine cutting-edge GNN architectures specifically developed for user profiling, highlighting the typical data utilised in this context. Furthermore, ethical considerations and beyond-accuracy perspectives, e.g. fairness and explainability, will be discussed regarding the potential applications of GNNs in user profiling. During the hands-on session, participants will gain practical insights into constructing and training recent GNN models for user profiling using open-source tools and publicly available datasets. The audience will actively explore the impact of these models through case studies focused on bias analysis and explanations of user profiles. To conclude the tutorial, we will analyse existing and emerging challenges in the field and discuss future research directions.

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 profiles. However, each GNN-based user profiling approach has its own way of processing information, thus creating heterogeneity that does not favour the benchmarking of these techniques. To overcome this issue, we present FairUP, a framework that standardises the input needed to run three state-of-the-art GNN-based models for user profiling tasks. Moreover, given the importance that algorithmic fairness is getting in the evaluation of machine learning systems, FairUP includes two additional components to (1) analyse pre-processing and post-processing fairness and (2) mitigate the potential presence of unfairness in the original datasets through three pre-processing debiasing techniques. The framework, while extensible in multiple directions, in its first version, allows the user to conduct experiments on four real-world datasets. The source code is available at https://link.erasmopurif.com/FairUP-source-code, and the web application is available at https://link.erasmopurif.com/FairUP.

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 capability to predict user features (such as gender or age) is evaluated. In this work, we perform a beyond-accuracy analysis of the state-of-the-art approaches to assess the presence of disparate impact and disparate mistreatment, meaning that users characterised by a given sensitive feature are unintentionally, but systematically, classified worse than their counterparts. Our analysis on two real-world datasets shows that different user profiling paradigms can impact fairness results. The source code and the preprocessed datasets are available at: https://github.com/erasmopurif/do_gnns_build_fair_models.

To satiate the comprehensive information need of users, retrieval systems surpassing the boundaries of language are inevitable in the present digital space in the wake of an ever-rising multilingualism. This work presents the first-of-its-kind Bilingual Text Retrieval Explanations (BiTe-REx) aimed at users performing competitor or wage analysis in the automotive domain. BiTe-REx supports users to gather a more comprehensive picture of their query by retrieving results regardless of the query language and enables them to make a more informed decision by exposing how the underlying model judges the relevance of documents. With a user study, we demonstrate statistically significant results on the understandability and helpfulness of the explanations provided by the system.

Context-aware information is widely available in various ways and is becoming more and more important for enhancing retrieval performance and recommendation results. The current main issue to cope with is not only recommending or retrieving the most relevant items and content, but defining them ad hoc. Other relevant issues include personalizing and adapting the information and the way it is displayed to the user's current situation and interests. Ubiquitous computing further provides new means for capturing user feedback on items and providing information.

The SPIM workshop focuses especially on people that are working on the social or semantic Web, machine learning, user modeling, recommender systems, information retrieval, semantic interaction, or their combination. The goal is to bring together researchers and practitioners to initiating discussions on the different requirements and challenges coming with the social and semantic Web for personalized information retrieval systems. The workshop aims at improving the exchange of ideas between the different research communities and practitioners involved in the research on semantic personalized information management.

The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.

CARS 2012 builds upon the success of the three previous editions held in conjunction with the 3rd to 5th ACM Conferences on Recommender Systems from 2009 to 2011. The 1st CARS Workshop was held in New York, NY, USA, whereas Barcelona, Spain, was home of the 2nd CARS Workshop in 2010. In 2011, the 3rd CARS workshop was held in Chicago, IL, USA.

This paper provides an overview of CAMRa2011, the second edition of the Challenge on Context-Aware Movie Recommendation. The challenge attracted a large number of participants to work on the challenge tracks, which this time focused on group related recommendation aspects.

CARS and CAMRa were organized under the Context-awareness in Recommendation Systems special event and gathered academic researchers as well as industrial practitioners in a workshop and challenge.

We study the application of spectral clustering, prediction and visualization methods to graphs with negatively weighted edges. We show that several characteristic matrices of graphs can be extended to graphs with positively and negatively weighted edges, giving signed spectral clustering methods, signed graph kernels and network visualization methods that apply to signed graphs. In particular, we review a signed variant of the graph Laplacian. We derive our results by considering random walks, graph clustering, graph drawing and electrical networks, showing that they all result in the same formalism for handling negatively weighted edges. We illustrate our methods using examples from social networks with negative edges and bipartite rating graphs.

In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or “Sense Folders” (prototype categories) derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user-specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 693–709, 2006.