DEvIR: Data Collection and Analysis for the Recommendation of Events and Itineraries

Distributed events such as multi-day festivals and conventions attract thousands of attendees. Their programs are usually very dense, which makes it difficult for users to select activities to perform. Recent works have proposed event and itinerary recommendation algorithms to solve this problem. Although several datasets have been made available for the evaluation of event recommendation algorithms, they do not suit well for the case of distributed events or itinerary recommendation. Based on the study of available online resources, we define dataset attributes required to perform event and itinerary recommendations in the context of distributed events, and discuss the compliance of existing datasets to these requirements. Revealing the lack of publicly available datasets with desired features, we describe a data collection process to acquire the publicly available data from a major comic book convention website. We present the characteristics of the collected data and discuss its usability for evaluating recommendation algorithms

Social Relations and Methods in Recommender Systems: A Systematic Review

With the constant growth of information, data sparsity problems, and cold start have become a complex problem in obtaining accurate recommendations. Currently, authors consider the user's historical behavior and find contextual information about the user, such as social relationships, time information, and location. In this work, a systematic review of the literature on recommender systems that use the information on social relationships between users was carried out. As the main findings, social relations were classified into three groups: trust, friend activities, and user interactions. Likewise, the collaborative filtering approach was the most used, and with the best results, considering the methods based on memory and model. The most used metrics that we found, and the recommendation methods studied in mobile applications are presented. The information provided by this study can be valuable to increase the precision of the recommendations

Improving The Students Writing Narrative Text Through Problem Based Learning At Ten Grade SMK 2 Satrya Budi Perdagangan

This research was using classroom action research which the cycle did in three times consist of precycle, cycleone, and cycletwo. The subject of there search was X-RPL students of Smk 2 Satrya Budi Perdagangan in the academic year of 2019/2010. The data was gotten from students’ achievement that was obtained froms tudents’ score in the test, and data observation from students’and teacher’s activities during teaching learning process took place. There was also documentation that was used to look for the data concerning matter sorth evariable that are taken in the form of then or photo in teaching learning process. This research showed that apply problem based learning to improve writing in narrative text could help the students. Problem based learning could be a solution for the student stomake narrative text easier because problem based learning pose situation as stimulation which the students could develop ideas. There was an improvement on students score too. The mean of the tests score was 43,4 in precycle, it was becoming 56,2 in the first cycle, and it wasb ecoming 83,9 in the second cycle. Finally the result of this research had the improvement of students’ writing in narrative text and the improvement in the students’ positive response after being taught by problem based learning. Hope fully this research can be a reference for teacher of English to teach narrative text and for next researcher for conducting next research

Designing interactive digital installation for human-human interaction in live music events

In the 21st century, there is a strong trend of the audience’s personal technology-dependent behavior in live music events, specifically music concerts and music festivals. This project, Interplaying, investigates the way technology is used to encourage the audience’s human-human interaction in such events in order to allow audience members to better engage in live-music-listening experience’s benefits such as socialization. My paper finds a lesson from human-human interaction in cultural/community festivals. Also, it does not criticize the presence of technology itself in the events. The project rather provides the way technology can bring the audience back to real environment from virtual communication. The prototype of Interplaying tangibly embodies the possibility of technology encouraging live social interaction amongst audience members

A survey of context-aware recommendation schemes in event-based social networks

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. In recent years, Event-based social network (EBSN) applications, such as Meetup and DoubanEvent, have received popularity and rapid growth. They provide convenient online platforms for users to create, publish, and organize social events, which will be held in physical places. Additionally, they not only support typical online social networking facilities (e.g., sharing comments and photos), but also promote face-to-face offline social interactions. To provide better service for users, Context-Aware Recommender Systems (CARS) in EBSNs have recently been singled out as a fascinating area of research. CARS in EBSNs provide the suitable recommendation to target users by incorporating the contextual factors into the recommendation process. This paper provides an overview on the development of CARS in EBSNs. We begin by illustrating the concept of the term context and the paradigms of conventional context-aware recommendation process. Subsequently, we introduce the formal definition of an EBSN, the characteristics of EBSNs, the challenges that are faced by CARS in EBSNs, and the implementation process of CARS in EBSNs. We also investigate which contextual factors are considered and how they are represented in the recommendation process. Next, we focus on the state-of-the-art computational techniques regarding CARS in EBSNs. We also overview the datasets and evaluation metrics for evaluation in this research area, and discuss the applications of context-aware recommendation in EBSNs. Finally, we point out research opportunities for the research community

Association of late childbearing with healthy longevity among the oldest-old in China

Statistical analysis of a large and unique longitudinal data set demonstrates that late childbearing after age 35 or 40 is significantly associated with survival and healthy survival among very old Chinese women and men. The association is stronger in oldest-old women than men. The estimates are adjusted for a variety of confounding factors of demographic characteristics, family support, social connections, health practices, and health conditions. Further analysis based on an extension of the Fixed Attribute Dynamics method shows that late childbearing is positively associated with long-term survival and healthy survival from ages 80-85 to 90-95 and 100-105. This association exists among oldest-old women and men, but, again, the effects are substantially stronger in women than men. We discuss four possible factors which may explain why late childbearing affects healthy longevity at advanced ages: (1) social factors; (2) biological changes caused by late pregnancy and delivery; (3) genetic and other biological characteristics; and (4) selection.

Social software for music

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200