Hybrid Recommender for Online Petitions with Social Network and Psycholinguistic Features

The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple ranking mechanism adopted by most of the online petition platforms cannot effectively link most petitions with those who are really concerned with them. According to previous studies online, petitions seriousness has been questioned due to the rare chance of succeeding. At most, less than 10% of online petitions get the chance to fulfill their causes. To solve this problem, we present a design of a novel recommender system (PETREC). It leverages social interaction features, psycholinguistic features, and latent topic features to provide a personalized ranking to different users. Hence, it can give users better petition recommendations fitting their unique concerns. We evaluate PETREC against matrix factorization collaborative filtering and content-based filtering with the bag of words (Bow) features as two baseline recommenders for benchmarking. PETREC prediction performance outperformed Matrix factorization collaborative filtering, Bow petition-based content filtering, and Bow user-based content filtering with 4.2%, 1.7%, and 2.8% respectively as improvements in Root Mean Square Error (RMSE). The recommendation system described in this paper has potential to improve the user experience of online petition platforms. Thus, it is possible that it could encourage more public participation. Eventually, it will help the citizens to make a real difference through actively participating in online petitions that are matching their personalized concerns

Accelerated incremental listwise learning to rank for collaborative filtering

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2017.O enorme volume de informação hoje em dia aumenta a complexidade e degrada a qualidade do processo de tomada de decisão. A fim de melhorar a qualidade das decisões, os sistemas de recomendação têm sido utilizados com resultados consideráveis. Nesse contexto, a filtragem colaborativa desempenha um papel ativo em superar o problema de sobrecarga de informação. Em um cenário em que novas avaliações são recebidas constantemente, um modelo estático torna-se ultrapassado rapidamente, portanto a velocidade de atualização do modelo é um fator crítico. Propomos um método de aprendizagem de ranqueamento incremental acelerado para filtragem colaborativa. Para atingir esse objetivo, aplicamos uma técnica de aceleração a uma abordagem de aprendizado incremental para filtragem colaborativa. Resultados em conjuntos de dados reais confirmam que o algoritmo proposto é mais rápido no processo de aprendizagem mantendo a precisão do modelo.Abstract : The enormous volume of information nowadays increases the complexity of the decision-making process and degrades the quality of decisions. In order to improve the quality of decisions, recommender systems have been applied with significant results. In this context, the collaborative filtering technique plays an active role overcoming the information overload problem. In a scenario where new ratings have been received constantly, a static model becomes outdated quickly, hence the rate of update of the model is a critical factor. We propose an accelerated incremental listwise learning to rank approach for collaborative filtering. To achieve this, we apply an acceleration technique to an incremental collaborative filtering approach. Results on real word datasets show that our proposal accelerates the learning process and keeps the accuracy of the model

SHELDON Smart habitat for the elderly.

An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare