Abstract
The development of relationships by private non-profit organizations (NPOs) with companies is fundamental to their sustainability. However, relational marketing and the definition of fundraising and communication strategies tend to be under-explored, resulting in punctual relationships with donors. This paper proposes to explore ways of developing relationships between NPOs and companies. After a systematization of the main contributions present in the literature, a qualitative and exploratory study was presented, in which semi-structured interviews were conducted with 8 NPO managers with different fields of activity and 16 managers of small, medium and large companies, all with headquarters in the district of Aveiro. The results show that what drives companies to support NPOs are essentially non-material benefits, such as the emotional impact on employees and other stakeholders. There are clear differences between small and large companies, the latter being more prone to formal relations. It was verified, however, that even the occasional supports can be recurrent and repeated, lacking the capacity to protocol the relation. Overall, the data offer interesting clues to NPO staff, namely resulting from a comparison of the different perspectives of the participants.

Abstract
This research aims to contribute to the understanding of how the experience influences music festival participants' satisfaction, and the latter affects future behavior. A quantitative methodology was adopted using a questionnaire as research instrument, which was applied to 240 participants in the Rock in Rio Lisbon festival 2014. The results point to a positive relation between satisfaction and the different dimensions of experience, including the social one that was developed for the present study. © 2018. revistaESPACIOS.com.

Abstract
A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled data, semi-supervised approaches such as Co-training and Self-training have been created to benefit from input information that is contained in unlabeled data. However, these approaches have been applied to classification and batch training scenarios. Due to these facts, this paper presents a comparison between Co-training and Self-learning methods for single-target regression in data streams. Rules learning is used in this context since this methodology enables to explore the input information. The experimental evaluation consisted of a comparison between the real standard scenario where all unlabeled data is rejected and scenarios where unlabeled data is used to improve the regression model. Results show evidences of better performance in terms of error reduction and in high level of unlabeled examples in the stream. Despite this fact, the improvements are not expressive.

Abstract
In a single-target regression context, some important systems based on data streaming produce huge quantities of unlabeled data (without output value), of which label assignment may be impossible, time consuming or expensive. Semi-supervised methods, that include the co-training approach, were proposed to use the input information of the unlabeled examples in the improvement of models and predictions. In the literature, the co-training methods are essentially applied to classification and operate in batch mode. Due to these facts, this work proposes a co-training online algorithm for single-target regression to perform model improvement with unlabeled data. This work is also the first-step for the development of online multi-target regressor that create models for multiple outputs simultaneously. The experimental framework compared the performance of this method, when it rejects unalabeled data and when it uses unlabeled data with different parametrization in the training. The results suggest that the co-training method regressor predicts better when a portion of unlabeled examples is used. However, the prediction improvements are relatively small. © Springer International Publishing AG 2017.

Abstract
Given the large volumes and dynamics of data that recommender systems currently have to deal with, we look at online stream based approaches that are able to cope with high throughput observations. In this paper we describe work on incremental neighborhood based and incremental matrix factorization approaches for binary ratings, starting with a general introduction, looking at various approaches and describing existing enhancements. We refer to recent work on forgetting techniques and multidimensional recommendation. We will also focus on adequate procedures for the evaluation of online recommender algorithms.

Abstract
Online recommender systems often deal with continuous, potentially fast and unbounded flows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms that learn from data streams. We evaluate online bagging with an incremental matrix factorization algorithm for top-N recommendation with positiveonly user feedback, often known as binary ratings. Our results show that online bagging is able to improve accuracy up to 35% over the baseline, with small computational overhead.