Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Phantom cascades: The effect of hidden nodes on information diffusion

Research on information diffusion generally assumes complete knowledge of the underlying network. However, in the presence of factors such as increasing privacy awareness, restrictions on application programming interfaces (APIs) and sampling strategies, this assumption rarely holds in the real world which in turn leads to an underestimation of the size of information cascades. In this work we study the effect of hidden network structure on information diffusion processes. We characterise information cascades through activation paths traversing visible and hidden parts of the network. We quantify diffusion estimation error while varying the amount of hidden structure in five empirical and synthetic network datasets and demonstrate the effect of topological properties on this error. Finally, we suggest practical recommendations for practitioners and propose a model to predict the cascade size with minimal information regarding the underlying network.Comment: Preprint submitted to Elsevier Computer Communication

Exploiting the conceptual space in hybrid recommender systems: a semantic-based approach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 200

Content-awareness and graph-based ranking for tag recommendation in folksonomies

Tag recommendation algorithms aid the social tagging process in many userdriven document indexing applications, such as social bookmarking and publication sharing websites. This thesis gives an overview of existing tag recommendation methods and proposes novel approaches that address the new document problem and the task of ranking tags. The focus is on graph-based methods such as Folk- Rank that apply weight spreading algorithms to a graph representation of the folksonomy. In order to suggest tags for previously untagged documents, extensions are presented that introduce content into the recommendation process as an additional information source. To address the problem of ranking tags, an in-depth analysis of graph models as well as ranking algorithms is conducted. Implicit assumptions made by the widely-used graph model of the folksonomy are highlighted and an improved model is proposed that captures the characteristics of the social tagging data more accurately. Additionally, issues in the tag rank computation of FolkRank are analysed and an adapted weight spreading approach for social tagging data is presented. Moreover, the applicability of conventional weight spreading methods to data from the social tagging domain is examined in detail. Finally, indications of implicit negative feedback in the data structure of folksonomies are analysed and novel approaches of identifying negative relationships are presented. By exploiting the three-dimensional characteristics of social tagging data the proposed metrics are based on stronger evidence and provide reliable measures of negative feedback. Including content into the tag recommendation process leads to a significant increase in recommendation accuracy on real-world datasets. The proposed adaptations to graph models and ranking algorithms result in more accurate and computationally less expensive recommenders. Moreover, new insights into the fundamental characteristics of social tagging data are revealed and a novel data interpretation that takes negative feedback into account is proposed

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

Exploiting the impact of user-generated content on brand coolness and consumer brand engagement: A text-mining approach

This dissertation aims to comprehend the impact of deploying user-generated content (UGC) campaigns on consumers’ perceptions of brand coolness and consumer brand engagement. The trending concept of coolness in the beauty industry is studied through electronic word of mouth to understand if brands encouraging their users to post about their brand experiences leads to consumers perceiving them as cool and engaging more positively through those publications. The methodology in use is a netnography, along with a sentiment analysis technique. The analysis consisted in observing the interactions, incited by a user-generated content campaign led by a prestigious beauty brand - Drunk Elephant, between the brand and its online brand community on the social network Instagram for one year to avoid seasonal phenomena. The comments were retrieved using a text-mining tool and analyzed through Natural Language Processing according to their sentiment polarity, and trending topics identified. The data retrieved from the year of 2019 amounted to 67 321 interactions. Results show consumers’ perceptions of coolness can be positively influenced by adopting UGC campaigns, which can also lead to positive consumer brand engagement. Not only do these campaigns generate brand awareness, but they stimulate brand community’s expansion and influence consumers’ perceptions towards the brand. Beauty brands seeking to grow their status of coolness and consumer interactions should consider implementing user-generated content campaigns, as keeping up with the trends in the market is not only regarded as cool but is necessary to remain relevant in the ever-changing marketplace beauty has proven itself to be.Esta dissertação visa entender o impacto da utilização de campanhas de conteúdo gerado pelos utilizadores nas perceções dos consumidores da coolness de uma marca e interações entre marca e consumidores. A tendência coolness na indústria da beleza é examinada através de electronic word-of-mouth para compreender se encorajar os utilizadores a partilhar conteúdo sobre as suas experiências com as marcas, os leva a pensar na marca como cool e a interagir mais com essas publicações. A metodologia usada é uma análise netnográfica em conjunto com uma técnica de análise sentimental. A análise foi conduzida sob interações textuais, incitadas pela campanha da marca de prestígio de beleza – Drunk Elephant, entre a marca e a sua comunidade online na rede social Instagram durante um ano para evitar fenómenos sazonais. Os comentários foram extraídos por text mining e analisados através de processamento de linguagem natural, tendo em conta a polaridade do seu sentimento, e tópicos mais frequentes identificados. Os dados retirados do ano de 2019 totalizaram 67 321 interações. Os resultados demonstram que as perceções de coolness do consumidor podem ser positivamente influenciadas adotando o uso destas campanhas e podem conduzir a interações positivas. Não só estas campanhas criam visibilidade para a marca, como encorajam a expansão da comunidade da marca e influenciam as perceções dos seus consumidores. Marcas na indústria da beleza que procuram aumentar a sua coolness e interações com os consumidores devem considerar implementar campanhas de conteúdo gerado pelos utilizadores, de maneira a manter-se atuais num mercado em constante transformação

Features for Killer Apps from a Semantic Web Perspective

There are certain features that that distinguish killer apps from other ordinary applications. This chapter examines those features in the context of the semantic web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing semantic web applications. Killer apps are highly tranformative technologies that create new e-commerce venues and widespread patterns of behaviour. Information technology, generally, and the Web, in particular, have benefited from killer apps to create new networks of users and increase its value. The semantic web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. The authors hope that this chapter will help to highlight some of the common ingredients of killer apps in e-commerce, and discuss how such applications might emerge in the semantic web

Towards Nootropia : a non-linear approach to adaptive document filtering

In recent years, it has become increasingly difficult for users to find relevant information within the accessible glut. Research in Information Filtering (IF) tackles this problem through a tailored representation of the user interests, a user profile. Traditionally, IF inherits techniques from the related and more well established domains of Information Retrieval and Text Categorisation. These include, linear profile representations that exclude term dependencies and may only effectively represent a single topic of interest, and linear learning algorithms that achieve a steady profile adaptation pace. We argue that these practices are not attuned to the dynamic nature of user interests. A user may be interested in more than one topic in parallel, and both frequent variations and occasional radical changes of interests are inevitable over time. With our experimental system "Nootropia", we achieve adaptive document filtering with a single, multi-topic user profile. A hierarchical term network that takes into account topical and lexical correlations between terms and identifies topic-subtopic relations between them, is used to represent a user's multiple topics of interest and distinguish between them. A series of non-linear document evaluation functions is then established on the hierarchical network. Experiments using a variation of TREC's routing subtask to test the ability of a single profile to represent two and three topics of interest, reveal the approach's superiority over a linear profile representation. Adaptation of this single, multi-topic profile to a variety of changes in the user interests, is achieved through a process of self-organisation that constantly readjusts the profile stucturally, in response to user feedback. We used virtual users and another variation of TREC's routing subtask to test the profile on two learning and two forgetting tasks. The results clearly indicate the profile's ability to adapt to both frequent variations and radical changes in user interests