1,720,976 research outputs found
Discovering prerequisite relations from educational documents through word embeddings
No full textInferring prerequisite relations among educational documents, in terms of prior knowledge required to understand and complete assignments about certain topics, is a crucial task for instructional designers and teachers. Massive open online courses, electronic textbooks, public encyclopedias and repositories of learning objects and other forms of informative content create a huge availability of educational material, which can be exploited in online platforms for distance education, both for recommending specific resources and personalized learning paths. But public taxonomies of prerequisites, or learning object metadata useful to trace down prerequisites are not generally available. A description of a new approach for prerequisite discovering in educational documents is given. It is based on word embeddings, that is, statistical language models for the representation of text-based learning objects in low-dimensional latent spaces. It takes advantage of the latent representations to identify prerequisites in a binary classification setting. The accuracy of the approach is validated by means of an experimental benchmark covering multiple datasets of educational material
Personalized weight loss strategies by mining activity tracker data
No full textWearable devices make self-monitoring easier by the users, who usually tend to increase physical activity and weight loss maintenance over time. But in terms of behavior adaptation to these goals, these devices do not provide specific features beyond monitoring the achievement of daily goals, such as a number of steps or miles walked and caloric outtake. The purpose of this study is twofold. By analyzing a large dataset of signals collected by these devices, we identify significant clusters of similar behavior patterns related to user physical activities. We then examine specific patterns of step count in the context of recommendation of habits that more likely give rise to weight loss effects. The evaluation of the effectiveness of these personalized recommendations, based on a comparative study, proves how a recommender system based on the reinforcement learning paradigm is able to guarantee better performance for this task by balancing the trade-off between long-term and short-term rewards
Tourism Recommender Systems as a Vehicle for Social and Cultural Inclusion
No full textRecommender systems for tourism have become so popular that our smart phones are now full of applications that can suggest customized itineraries anywhere and anytime. Most of them, however, recommend similar itineraries, usually even in the same overcrowded areas. In this article, we present the concept of an integrated framework for cultural tourism with different characteristics. Such a framework can propose alternative customized itineraries to favor cultural and social inclusion of visitors with local residents, for example, in urban suburbs or agricultural and industrial regions. Therefore, the system has to provide user interfaces to enable organizations, local enterprises, and visitors to analyze and exploit rich open data sources. In this way, local institutions could better plan and handle cultural tourism and public resources. Small businesses could cost-effectively promote their services. Visitors could receive personalized routes with knowledge related to local communities, cultures, traditions, and others. © 202
A Machine Learning Approach to Prediction of Online Reviews Reliability
No full textThe Internet accompanies us in every moment of our lives, supporting us in many ways. Among these, it helps us when we want to choose the best services and products. So when it comes to picking a movie to watch, a restaurant to eat in, a hotel to stay in, or a product to buy, we grab our smartphone and visit one of the countless sites where users can report their experiences and read those of others. However, as often happens, even in this case there are possible scams of which we must beware. In this paper, we propose a machine learning system for predicting the reliability of online reviews. Specifically, our system collects reviews from Amazon, extracts various features, and gives them as input to an ensemble learning system based on three anomaly detection algorithms. To demonstrate the benefits of our approach, we report the results of a comparative analysis with some state-of-the-art systems using the data collected by ReviewMeta. These results have allowed us to realize how widespread the phenomenon of online fake reviews is
Using Social Media for Personalizing the Cultural Heritage Experience
No full textThis article presents a personalized recommendation approach of textual and multimedia resources related to artistic and cultural points of interest (POIs). This approach exploits linked open data to retrieve content related to POIs and social media to personalize their recommendation to the target user. The similarity evaluation between the social user profile and the related material occurs based on the classic doc2vec model. A preliminary comparative analysis conducted on 20 real users showed encouraging experimental results in terms of perceived accuracy and beyond-accuracy metrics
Cross-domain recommendation for enhancing cultural heritage experience
No full textIn this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked open data (LOD), by following semantic links in the LOD graph to identify movies, books, and music artists/songs related to that specific POI. This content is subsequently reranked based on the activity of the user and her friends on social media (i.e., Facebook), in order to provide personalized suggestions
SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE)
No full textThis is the first edition of the SOcial and Cultural IntegrAtion with PersonaLIZEd Interfaces (SOCIALIZE) workshop. The main goal is to bring together all those interested in the development of interactive techniques that may contribute to foster the social and cultural inclusion of a broad range of users. More specifically, we intend to attract research that takes into account the interaction peculiarities typical of different realities, with a focus on disadvantaged and at-risk categories (e.g., refugees and migrants) and vulnerable groups (e.g., children, elderly, autistic and disabled people). Among others, we are also interested in human-robot interaction techniques aimed at the development of social robots, that is, autonomous robots that interact with people by engaging in social-affective behaviors, abilities, and rules related to their collaborative role
Evaluating the efficacy of traditional fitness tracker recommendations
No full textWearable devices make self-monitoring easier by the users, who usually tend to increase physical activity and weight loss maintenance over time. But in terms of behavior adaptation to these goals, these devices do not provide specific features beyond monitoring the achievement of daily goals, such as a number of steps or miles walked, and caloric outtake. The purpose of this study is to evaluate the efficacy of the recommendations provided by traditional fitness tracker apps with respect to weight loss scenarios
HOLMES: a prototype for the targeted research of information about hi-tech companies
No full textA system prototype for intelligent agents is presented. The system, called Holmes, is specialized in adaptive information filtering and searching of interesting information on the Web. It allows for the execution of searches by exploiting both indications provided by traditional search engines and autonomous exploration. In both cases, we use the model of the user, in the first case to make the proposed results more significant by modifying the proposed order, and in the second one to guide the exploration
Cultural Impact on Digital Ecosystems: Exploring User Activity in Italy and the USA during the COVID-19 Pandemic
Full text linkThe COVID-19 pandemic significantly impacted people’s lives, leading to an unprecedented amount of data generated on the Internet. In this paper, we present the results of an in-depth analysis of user behavior in the digital ecosystem in Italy and the USA during the first six months of the pandemic. Our objective is to verify whether different cultures have been able to significantly impact the searches carried out by users online and their interactions on social networks
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