In this article we review the existing techniques in group recommender systems and we propose some improvement based on the study of the different individual behaviors when carrying out a decision-making process. Our method includes an analysis of group personality composition and trust between each group member to improve the accuracy of group recommenders. This way we simulate the argumentation process followed by groups of people when agreeing on a common activity in a more realistic way. Moreover, we reflect how they expect the system to behave in a long term recommendation process. This is achieved by including a memory of past recommendations that increases the satisfaction of users whose preferences have not been taken into account in previous recommendations.

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system's output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PS IE). Unlike other expert systems the PS IE proposal novelly includes explanations about the system's group recommendation and explanations about the group's social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on "tactful" explanations when addressing users' personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users' intent (likelihood) to follow the recommendations, users' satisfaction and the system's efficiency and trustworthiness.