Semantic Grounding Strategies for Tagbased Recommender Systems

Recommender systems usually operate on similarities between recommended items or users. Tag based recommender systems utilize similarities on tags. The tags are however mostly free user entered phrases. Therefore, similarities computed without their semantic groundings might lead to less relevant recommendations. In this paper, we study a semantic grounding used for tag similarity calculus. We show a comprehensive analysis of semantic grounding given by 20 ontologies from different domains. The study besides other things reveals that currently available OWL ontologies are very narrow and the percentage of the similarity expansions is rather small. WordNet scores slightly better as it is broader but not much as it does not support several semantic relationships. Furthermore, the study reveals that even with such number of expansions, the recommendations change considerably.Comment: 13 pages, 5 figure

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

The Impact of Digital Technologies on Memory and Memory Studies

With the widespread integration of smartphones, computers, and the internet, information access and processing have undergone significant changes. This paper investigates both positive and negative implications, acknowledging the extension of cognitive capacities through easy access to vast databases and external memory aids while also addressing concerns about diminished memory consolidation and reliance on shallow encoding strategies. Examining the interdisciplinary field of memory studies, the study also highlights collaborative efforts among scholars in psychology, neuroscience, sociology, and information science to comprehend the impact of digital technologies on memory, and emphasizes the challenges and future directions in memory research, including issues like digital amnesia, information overload, and privacy concerns. Overall, the paper underscores the need for understanding the relationship between human memory and digital tools, enabling the development of strategies to enhance memory, counteract potential adverse effects, and promote a balanced utilization of digital resources in memory-related tasks

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes. Copyright © 2022 von Hoyer, Hoppe, Kammerer, Otto, Pardi, Rokicki, Yu, Dietze, Ewerth and Holtz

The Search as Learning Spaceship: Toward a Comprehensive Model of Psychological and Technological Facets of Search as Learning

Using a Web search engine is one of today’s most frequent activities. Exploratory search activities which are carried out in order to gain knowledge are conceptualized and denoted as Search as Learning (SAL). In this paper, we introduce a novel framework model which incorporates the perspective of both psychology and computer science to describe the search as learning process by reviewing recent literature. The main entities of the model are the learner who is surrounded by a specific learning context, the interface that mediates between the learner and the information environment, the information retrieval (IR) backend which manages the processes between the interface and the set of Web resources, that is, the collective Web knowledge represented in resources of different modalities. At first, we provide an overview of the current state of the art with regard to the five main entities of our model, before we outline areas of future research to improve our understanding of search as learning processes

Digitaalse teadmuse arhiveerimine – teoreetilis-praktiline uurimistöö Rahvusarhiivi näitel

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Digitaalse informatsiooni pidevalt kiirenev juurdekasv on aidanud rõhutada ka olulise informatsiooni säilitamise vajadust. Säilitamine ei tähenda siinkohal pelgalt füüsilist varundamist, vaid ka informatsiooni kasutatavuse ja mõistetavuse tagamist. See tähendab, et tegelikkuses on vaja hoolitseda ka selle eest, et meil oleks olemas vajalik riist- ja tarkvara arhiveeritud teabe kasutamiseks. Kui seda ei ole, siis saab mõningatel juhtudel kasutada emulaatoreid, mis matkivad konkreetset aegunud süsteemi ja võimaldavad niiviisi vanu faile avada. Samas, kui tehnoloogia iganemist on võimalik ette näha, siis oleks mõistlik failid juba varakult püsivamasse vormingusse ümber konverteerida või andmekandja kaasaegsema vastu vahetada. Nii emuleerimine, konverteerimine kui ka nende kombineerimine aitavad säilitada informatsiooni kasutatavust, kuid ei pruugi tagada autentset mõistetavust, kuna digitaalse teabe esitus sõltub alati säilitatud bittide tõlgendamisest. Näiteks, kui luua WordPad tarkvara abil üks dokument ja avada seesama dokument Hex Editor Neo abil, siis näeme seda faili kahendkujul, Notepad++ näitab RTFi kodeeringut, Microsoft Word 2010 ja LibreOffice Writeri esitustes võime märgata juba mitmeid erinevusi. Kõik eelloetletud esitused on tehnoloogilises mõttes õiged. Faili avamisel veateateid ei teki, sest tarkvara seisukohast lähtudes peavadki esitused sellised olema. Siinjuures oluline rõhutada, et ka korrektne esitus võib jääda kasutajale mõistetamatuks – see, et andmed on säilinud, et neid on võimalik lugeda ja esitada, ei garanteeri paraku, et neid õigesti mõistetakse. Mõistetavuse tagamiseks tuleb alati arvestada ka lõppkasutajaskonnaga. Seetõttu uuribki antud töö võimalusi, kuidas toetada teadmuse (mõistetava informatsiooni) digitaalset arhiveerimist tuginedes eelkõige parimale praktikale, praktilistele eksperimentidele Rahvusarhiivis ja interdistsiplinaarsetele (nt infotehnoloogia kombineerimine arhiivindusega) võtetele.Digital preservation of knowledge is a very broad and complex research area. Many aspects are still open for research. According to the literature, the accessibility and usability of digital information have been more investigated than the comprehensibility of important digital information over time. Although there are remedies (e.g. emulation and migration) for mitigating the risks related to the accessibility and usability, the question how to guarantee understandability/comprehensibility of archived information is still ongoing research. Understanding digital information first requires a representation of the archived information, so that a user could then interpret and understand it. However, it is a not-so-well-known fact that the digital information does not have any fixed representation before involving some software. For example, if we create a document in WordPad and open the same file in Hex Editor Neo software, then we will see the binary representation which is also correct but not suitable for human users, as humans are not used to interpreting binary codes. When we open that file in Notepad++, then we can see the structure of the RTF coding. Again, this is the correct interpretation of this file, but not understandable for the ordinary user, as it shows the technical view of the file format structure. When we open that file in Microsoft Word 2010 or LibreOffice Writer, then we will notice some changes, although the original bits are the same and no errors are displayed by the software. Thus, all representations are technologically correct and no errors will be displayed to the user when they are opening this file. It is important to emphasise that in some cases even the original representation may be not understandable to the users. Therefore, it is important to know who the main users of the archives are and to ensure that the archived objects are independently understandable to that community over the long term. This dissertation will therefore research meaningful use of digital objects by taking into account the designated users’ knowledge and Open Archival Information System (OAIS) model. The research also includes several practical experimental projects at the National Archives of Estonia which will test some important parts of the theoretical work

Personalized information retrieval based on time-sensitive user profile

Les moteurs de recherche, largement utilisés dans différents domaines, sont devenus la principale source d'information pour de nombreux utilisateurs. Cependant, les Systèmes de Recherche d'Information (SRI) font face à de nouveaux défis liés à la croissance et à la diversité des données disponibles. Un SRI analyse la requête soumise par l'utilisateur et explore des collections de données de nature non structurée ou semi-structurée (par exemple : texte, image, vidéo, page Web, etc.) afin de fournir des résultats qui correspondent le mieux à son intention et ses intérêts. Afin d'atteindre cet objectif, au lieu de prendre en considération l'appariement requête-document uniquement, les SRI s'intéressent aussi au contexte de l'utilisateur. En effet, le profil utilisateur a été considéré dans la littérature comme l'élément contextuel le plus important permettant d'améliorer la pertinence de la recherche. Il est intégré dans le processus de recherche d'information afin d'améliorer l'expérience utilisateur en recherchant des informations spécifiques. Comme le facteur temps a gagné beaucoup d'importance ces dernières années, la dynamique temporelle est introduite pour étudier l'évolution du profil utilisateur qui consiste principalement à saisir les changements du comportement, des intérêts et des préférences de l'utilisateur en fonction du temps et à actualiser le profil en conséquence. Les travaux antérieurs ont distingué deux types de profils utilisateurs : les profils à court-terme et ceux à long-terme. Le premier type de profil est limité aux intérêts liés aux activités actuelles de l'utilisateur tandis que le second représente les intérêts persistants de l'utilisateur extraits de ses activités antérieures tout en excluant les intérêts récents. Toutefois, pour les utilisateurs qui ne sont pas très actifs dont les activités sont peu nombreuses et séparées dans le temps, le profil à court-terme peut éliminer des résultats pertinents qui sont davantage liés à leurs intérêts personnels. Pour les utilisateurs qui sont très actifs, l'agrégation des activités récentes sans ignorer les intérêts anciens serait très intéressante parce que ce type de profil est généralement en évolution au fil du temps. Contrairement à ces approches, nous proposons, dans cette thèse, un profil utilisateur générique et sensible au temps qui est implicitement construit comme un vecteur de termes pondérés afin de trouver un compromis en unifiant les intérêts récents et anciens. Les informations du profil utilisateur peuvent être extraites à partir de sources multiples. Parmi les méthodes les plus prometteuses, nous proposons d'utiliser, d'une part, l'historique de recherche, et d'autre part les médias sociaux. En effet, les données de l'historique de recherche peuvent être extraites implicitement sans aucun effort de l'utilisateur et comprennent les requêtes émises, les résultats correspondants, les requêtes reformulées et les données de clics qui ont un potentiel de retour de pertinence/rétroaction. Par ailleurs, la popularité des médias sociaux permet d'en faire une source inestimable de données utilisées par les utilisateurs pour exprimer, partager et marquer comme favori le contenu qui les intéresse. En premier lieu, nous avons modélisé le profil utilisateur utilisateur non seulement en fonction du contenu de ses activités mais aussi de leur fraîcheur en supposant que les termes utilisés récemment dans les activités de l'utilisateur contiennent de nouveaux intérêts, préférences et pensées et doivent être pris en considération plus que les anciens intérêts surtout que de nombreux travaux antérieurs ont prouvé que l'intérêt de l'utilisateur diminue avec le temps. Nous avons modélisé le profil utilisateur sensible au temps en fonction d'un ensemble de données collectées de Twitter (un réseau social et un service de microblogging) et nous l'avons intégré dans le processus de reclassement afin de personnaliser les résultats standards en fonction des intérêts de l'utilisateur.En second lieu, nous avons étudié la dynamique temporelle dans le cadre de la session de recherche où les requêtes récentes soumises par l'utilisateur contiennent des informations supplémentaires permettant de mieux expliquer l'intention de l'utilisateur et prouvant qu'il n'a pas trouvé les informations recherchées à partir des requêtes précédentes.Ainsi, nous avons considéré les interactions récentes et récurrentes au sein d'une session de recherche en donnant plus d'importance aux termes apparus dans les requêtes récentes et leurs résultats cliqués. Nos expérimentations sont basés sur la tâche Session TREC 2013 et la collection ClueWeb12 qui ont montré l'efficacité de notre approche par rapport à celles de l'état de l'art. Au terme de ces différentes contributions et expérimentations, nous prouvons que notre modèle générique de profil utilisateur sensible au temps assure une meilleure performance de personnalisation et aide à analyser le comportement des utilisateurs dans les contextes de session de recherche et de médias sociaux.Recently, search engines have become the main source of information for many users and have been widely used in different fields. However, Information Retrieval Systems (IRS) face new challenges due to the growth and diversity of available data. An IRS analyses the query submitted by the user and explores collections of data with unstructured or semi-structured nature (e.g. text, image, video, Web page etc.) in order to deliver items that best match his/her intent and interests. In order to achieve this goal, we have moved from considering the query-document matching to consider the user context. In fact, the user profile has been considered, in the literature, as the most important contextual element which can improve the accuracy of the search. It is integrated in the process of information retrieval in order to improve the user experience while searching for specific information. As time factor has gained increasing importance in recent years, the temporal dynamics are introduced to study the user profile evolution that consists mainly in capturing the changes of the user behavior, interests and preferences, and updating the profile accordingly. Prior work used to discern short-term and long-term profiles. The first profile type is limited to interests related to the user's current activities while the second one represents user's persisting interests extracted from his prior activities excluding the current ones. However, for users who are not very active, the short-term profile can eliminate relevant results which are more related to their personal interests. This is because their activities are few and separated over time. For users who are very active, the aggregation of recent activities without ignoring the old interests would be very interesting because this kind of profile is usually changing over time. Unlike those approaches, we propose, in this thesis, a generic time-sensitive user profile that is implicitly constructed as a vector of weighted terms in order to find a trade-off by unifying both current and recurrent interests. User profile information can be extracted from multiple sources. Among the most promising ones, we propose to use, on the one hand, searching history. Data from searching history can be extracted implicitly without any effort from the user and includes issued queries, their corresponding results, reformulated queries and click-through data that has relevance feedback potential. On the other hand, the popularity of Social Media makes it as an invaluable source of data used by users to express, share and mark as favorite the content that interests them. First, we modeled a user profile not only according to the content of his activities but also to their freshness under the assumption that terms used recently in the user's activities contain new interests, preferences and thoughts and should be considered more than old interests. In fact, many prior works have proved that the user interest is decreasing as time goes by. In order to evaluate the time-sensitive user profile, we used a set of data collected from Twitter, i.e a social networking and microblogging service. Then, we apply our re-ranking process to a Web search system in order to adapt the user's online interests to the original retrieved results. Second, we studied the temporal dynamics within session search where recent submitted queries contain additional information explaining better the user intent and prove that the user hasn't found the information sought from previous submitted ones. We integrated current and recurrent interactions within a unique session model giving more importance to terms appeared in recently submitted queries and clicked results. We conducted experiments using the 2013 TREC Session track and the ClueWeb12 collection that showed the effectiveness of our approach compared to state-of-the-art ones. Overall, in those different contributions and experiments, we prove that our time-sensitive user profile insures better performance of personalization and helps to analyze user behavior in both session search and social media contexts

Toward a combinatorial analysis and parametric study to build time-aware social profiles.

Research has shown the effectiveness of inferring user interests from social neighbors, also called "social profiling". However, the evolution in the social profile is not widely taken into consideration. To overcome this drawback, we propose a time-aware social profiling method that considers the temporal factors of the information and the relationships between the user and his/her social neighbors. This method aims at weighting user interests in the social profile, by applying a time decay function. The temporal score of a given interest is computed by combining the temporal score of information used to extract the interests with the temporal score of individuals who share the information in the network. The experiments conducted on a co-authorship network, DBLP showed that the time-aware social profiling process applying our proposed time-aware method outperforms the existing time-agnostic social profiling process. The combinatorial analysis and the parametric study led us to observe that in the context of co-authorship network, the individual temporal score has more influence than the information temporal score. As this kind of network does not exhibit a rapid evolution of information and relationships, to obtain a relevant social profile, the information should be damped slowly