From implicit preferences to ratings: Video games recommendation based on collaborative filtering

This work studies and compares the performance of collaborative filtering algorithms, with the intent of proposing a videogame-oriented recommendation system. This system uses information from the video game platform Steam, which contains information about the game usage, corresponding to the implicit feedback that was later transformed into explicit feedback. These algorithms were implemented using the Surprise library, that allows to create and evaluate recommender systems that deal with explicit data. The algorithms are evaluated and compared with each other using metrics such as RSME, MAE, Precision@k, Recall@k and F1@k. We have concluded that computationally low demanding approaches can still obtain suitable results.info:eu-repo/semantics/acceptedVersio

Characterizing the personality of Twitter users based on their timeline information

Personality is a set of characteristics that differentiate a person from others. It can be identified by the words that people use in conversations or in publications that they do in social networks. Most existing work focuses on personality prediction analyzing English texts. In this study we analyzed publications of the Portuguese users of the social network Twitter. Taking into account the difficulties in sentiment classification that can be caused by the 140 character limit imposed on tweets, we decided to use different features and methods such as the quantity of followers, friends, locations, publication times, etc. to get a more precise picture of a personality. In this paper, we present methods by which the personality of a user can be predicted without any effort from the Twitter users. The personality can be accurately predicted through the publicly available information on Twitter profiles.info:eu-repo/semantics/publishedVersio

A simplified method to enhance the analysis for new information systems in corporate environments

The impact of efficient Information System Strategy Plans has proven crucial to modern-day corporations. However, during the analysis phase for a technical solution to fulfil an identified need in an enterprise, many teams tend to focus on a very issue-specific analysis and overlook its underlying global corporate impacts. On the other hand, it is difficult and time-expensive for these teams to analyse every existing corporate solution and how they are affected by their technical decisions. Moreover, such analysis still represents a significant organisational risk. To reduce such risks and perform a more efficient analysis we propose a simple method that considers an initial high-level analysis, focusing on the most common requirements of an enterprise in what concerns its Information Systems.info:eu-repo/semantics/acceptedVersio

Changes in Iron Metabolism Induced by Anti-Interleukin-6 Receptor Monoclonal Antibody are Associated with an Increased Risk of Infection

(1) Background: Treatment of patients with rheumatoid arthritis (RA) with an anti-IL-6 receptor (anti-IL-6R) monoclonal antibody (tocilizumab) has been found to influence iron metabolism. The objective of the present study was to ascertain whether changes in iron metabolism induced by anti-IL-6R biologic therapy were independently associated with an increased infection risk. (2) Methods: A prospective longitudinal study of patients with RA treated with tocilizumab was conducted. RA patients treated with an antitumor necrosis factor α monoclonal antibody were also included as a control group. The primary outcome was occurrence of infection during the first 24 months of biologic therapy. (3) Results: A total of 15 patients were included, with a mean age of 51.0 ± 4,1 and 73.3% (n = 11) female. A multivariate survival regression model, adjusted for confounding factors, was fitted for each of the iron metabolism variables. Hazard ratios for being above the median of each parameter was considered. Transferrin saturation above the median value (>32.1%) was associated with a higher infection risk (HR 4.3; 95%CI 1.0-19.69; p = 0.05). Similarly, although non-significantly, higher serum iron was strongly associated with infection occurrence. (4) Conclusions: This study identified a probable association between infection risk and higher serum iron and transferrin saturation in patients with RA on anti-IL-6R biologic therapy. We suggest that both these parameters should be considered relevant contributing factors for infection occurrence in patients on anti-IL-6R therapy.info:eu-repo/semantics/publishedVersio

Electron Spin Resonance of defects in the Haldane System Y(2)BaNiO(5)

We calculate the electron paramagnetic resonance (EPR) spectra of the antiferromagnetic spin-1 chain compound Y(2)BaNi(1-x)Mg(x)O(5) for different values of x and temperature T much lower than the Haldane gap (~100K). The low-energy spectrum of an anisotropic Heisenberg Hamiltonian, with all parameters determined from experiment, has been solved using DMRG. The observed EPR spectra are quantitatively reproduced by this model. The presence of end-chain S=1/2 states is clearly observed as the main peak in the spectrum and the remaining structure is completely understood.Comment: 5 pages, 4 figures include

Discovering trends in brand interest through topic models

Topic Modeling is a well-known unsupervised learning technique used when dealing with text data. It is used to discover latent patterns, called topics, in a collection of documents (corpus). This technique provides a convenient way to retrieve information from unclassified and unstructured text. Topic Modeling tasks have been performed for tracking events/topics/trends in different domains such as academic, public health, marketing, news, and so on. In this paper, we propose a framework for extracting topics from a large dataset of short messages, for brand interest tracking purposes. The framework consists training LDA topic models for each brand using time intervals, and then applying the model on aggregated documents. Additionally, we present a set of preprocessing tasks that helped to improve the topic models and the corresponding outputs. The experiments demonstrate that topic modeling can successfully track people’s discussions on Social Networks even in massive datasets, and ca pture those topics spiked by real-life events.info:eu-repo/semantics/acceptedVersio