iAggregator: Multidimensional Relevance Aggregation Based on a Fuzzy Operator

International audienceRecently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real-life applications. In this article, we present a new fuzzy-based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog,1 emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state-of-the-art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning-to-rank algorithms

Information Technology, Regime Stability and Democratic Meaningfulness: A Normative Evaluation of Present and Potential Trends

This inquiry explores the normative impact that the rise of Information Technology is having on society as viewed through the lenses of Social Choice and Democratic Theory. Information technology has drastically increased the amount of available information by increasing information about users, the flow of information to users and the flow of information between users through digital connectivity. This has resulted in a socially fragmenting “long tail” of media, a subversion of top-down institutions and has made for easier identification and mobilization of small and geographically dispersed groups. As understood through the Social Choice construct of multidimensionality, these trends have had both positive and negative normative implications for regime stability and democratic meaningfulness. The two negative normative effects of the rise of Information Technology are Rikerian meaninglessness and connectivity-driven regime instability. However, since these negative effects can be qualified or compensated for by the two positive impacts of democratic meaningfulness and stability-inducing pluralistic disequilibrium, this examination concludes that information technology has a positive net normative impact on society. If this is to continue to be the case, users and policy makers must be mindful of issues that could affect this balance, such as the digital divide and issues of diminishing digital privacy

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

TOWARDS EXPLAINING THE WILLINGNESS TO DISCLOSE PERSONAL SELF-TRACKING DATA TO SERVICE PROVIDERS

Users of digital self-tracking devices increasingly benefit from multiple services related to their self-tracking data. Simultaneously, service providers are dependent from these data to offer such services. Thereby, the willingness of users to provide such personal data heavily depends on benefits and risks associated with the disclosure. In this regard, the aim of our research is to investigate the factors influencing the willingness to disclose personal self-tracking data to service providers. So far, IS re-search has largely focused on private information disclosure in social media and little in the health and behavior context. To advance research in this area, we develop a conceptual model based on the privacy calculus by building on established information disclosure and privacy theories. With our re-search, we aim at contributing to both a better theoretical understanding in the fields of privacy and information disclosure and giving practical implications for service provider

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Using Semantic Web technologies in the development of data warehouses: A systematic mapping

The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043

Enriching e-learning metadata through digital library usage analysis

Purpose: In this paper we propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries. Methodology: We present experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment looks into raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process. Findings: Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish elationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means. Research limitations: During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also search for information outside the virtual campus. Not all of these usage data are considered in the model we propose. Further research needs to be done in order to get a complete view of the information search behavior of students for improving the users’ profile and creating better personalized services. Practical implications: In this paper we suggest how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community. Originality: Ever since the beginnings of libraries, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source

A context aware recommender system for tourism with ambient intelligence

Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais

Eliciting Touristic Profiles: A User Study on Picture Collections

Eliciting the preferences and needs of tourists is challenging, since people often have difficulties to explicitly express them, especially in the initial phase of travel planning. Recommender systems employed at the early stage of planning can therefore be very beneficial to the general satisfaction of a user. Previous studies have explored pictures as a tool of communication and as a way to implicitly deduce a traveller's preferences and needs. In this paper, we conduct a user study to verify previous claims and conceptual work on the feasibility of modelling travel interests from a selection of a user's pictures. We utilize fine-tuned convolutional neural networks to compute a vector representation of a picture, where each dimension corresponds to a travel behavioural pattern from the traditional Seven-Factor model. In our study, we followed strict privacy principles and did not save uploaded pictures after computing their vector representation. We aggregate the representations of the pictures of a user into a single user representation, i.e., touristic profile, using different strategies. In our user study with 81 participants, we let users adjust the predicted touristic profile and confirm the usefulness of our approach. Our results show that given a collection of pictures the touristic profile of a user can be determined.Comment: Accepted at UMAP 2020 (full paper