Digitalization and Information Management Mechanism of Sports Events Based on Multisensor Node Cooperative Perception Model

In the process of developing major sports events, how to guide providers and users to provide and utilize the archives information resources of major sports events and realize the interaction between them is an important problem to be solved urgently in the development of major sports events and the archive service of major sports events. By analyzing the present situation of archive service of major sports events, especially the analysis of the opposite dependent subjects of service providers and users, we can see that the continuous development of archive services for major sports events will inevitably lead to constant changes in user groups and user needs, guided by the theory of information retrieval, knowledge management, and media effect. According to the service model of archive service of major sports events, the archive service model of specific sports events is constructed. In this paper, four kinds of event recommendation models are applied to the collected marathon event data for experiments. Through experimental comparison, the effectiveness of content-based recommendation algorithm technology in the event network data set is verified, and an algorithm model suitable for marathon event recommendation is obtained. Experiments show that the comprehensive event recommendation model based on term frequency–inverse document frequency (TF-IDF) text weight and Race2vec entry sequence has the best recommendation performance on marathon event data set. According to the recommendation target of the event and the characteristics of the event data type, we can choose a single or comprehensive recommendation algorithm to build a model to realize the event recommendation.

An Intelligent and Data-Driven Mobile Platform for Youth Volunteer Management using Machine Learning and Data Analytics

In Lewis and Clark High School’s Key Club, meetings are always held in a crowded classroom. The system of eventsign up is inefficient and hinders members from joining events. This has led to students becoming discouraged fromjoining Key Club and often resulted in a lack of volunteers for important events. The club needed a more efficientway of connecting volunteers with volunteering opportunities. To solve this problem, we developed a Volunteer Match Mobile application using Dart and Flutter framework for Key Club to use. The next steps will be toadd a volunteer event recommendation and matching feature, utilizing the results from the research on machine learning models and algorithms in this paper.

Personalized Event Recommendation System

Many times, we receive a large number of notifications about various events, exhibitions, and meetups happening all around us that are irrelevant to us because we simply are not interested in them. Various people have their importance of things that they are interested in and be notified of all these events and from them searching for something that might interest them will take a lot of time and sometimes does not provide any meaningful information. In today’s world, there is no such existing facility that notifies us about the various events that are tailored to our interest strictly based on our web browsing history. Thus, we aim to create a Personalized Event Recommendation System that recommends the events that are sorted according to the user based on his/her interests using their browser history.

Event Recommendation System using Hybrid Method Based on Mobile Device

In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesn’t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.