YouTube API Research Articles

Calculus video recommender system

Mathematics had been taught from the lower level of education to the higher level of education. Basic mathematics had been exposed since pre-school to give a good foundation on mathematics so that they can learn better in primary school and also in secondary school. At a higher level of education, the basic calculus is offered and it is one of the core components in mathematics that needs for technical studies. With the advancement on information technology, the students are no longer bound to the face-to-face learning. A plenty of online sources are available to help them learn anytime they want. The text based documents and tutorial videos can be accessed easily. In this research, we had studied the existing recommender system features and the recommendation techniques. Here, we had emphasized on finding the tutorial video based on the user’s preferences particularly on basic calculus topics. A YouTube API is used to retrieve the basic calculus online videos. The YouTube API will send back a list of videos based on the searched keywords. The content-based recommendation technique known as keyword-based vector space model has been chosen because the results tend to show lots of similar videos than any other recommendation techniques. The keyword-based vector space model will convert the video title into a vector space and it will calculate the cross multiplication of the term frequency and the inverse document factor and it will also determine the closeness of the title by using the cosine similarity. The aim of the video recommender system is to help the students in searching an online video tutorial from the internet especially from YouTube and also to give a recommendation of a video based on it’s suitability.

The Development and Evaluation of a Network for Producing and Sharing Video Presentations

This paper describes the technology and methodology used in the development and evaluation of an online network to help instructors produce and share video presentations in a new and innovative way. The network offers an application and platform for recording and sharing video presentations. The application allows instructors to narrate and annotate PowerPoint slides and synchronize a variety of visual aids, including webcam footage, whiteboard, drawing board, and web browser. The platform uses YouTube API to upload and integrate videos with scrollable slide thumbnails. Quantitative and qualitative techniques were implemented to collect data and report on the usability of the network and effectiveness of video presentations. The results of usability evaluation showed that the network was perceived by instructors and students as effective, efficient, and relevance for self- production and sharing of video presentations. In addition, video presentations produced by participants met the criteria of quality of video presentations.

소셜 네트워크 데이터베이스를 이용한 퍼지 결정 기반의 추천 채널 시스템

사용자는 일반적으로 멀티미디어 소셜 서비스로부터 다른 사람들과 같은 결과를 제공받는다. 따라서 소셜 네트워크 안에 개인의 어려운 문제를 해결하기 위해 본 논문에서는 의사 결정 시스템 구축을 사용자의 활동, 사용자의 기분과 소셜 네트워크를 통한 사용자의 친구 관계 정보를 활용하는 방법을 제안한다. 사용자의 현재 기분 상태에 따라 시스템은 사용자에게 가장 적합한 영상을 유추한다. 이 시스템은 사용자가 이용하는 소셜 네트워크 데이터베이스에서 추출한 추천 방법의 집합을 측정하고, 가중치에 따라 모호한 값이 각각의 방법에 할당한다. 본 논문에 시스템에서는 퍼지 수집 솔루션을 찾아서 하위 집합들로 방법들을 분류하고, 가장 적절한 방법을 선택하기 위해 퍼지로직을 기반으로 상기 하위 집합을 결정한다. 마지막으로, YouTube API와 다양한 영상을 이용하여 시뮬레이션 실험을 진행하였다. 이 실험에서 채널 추천 시스템은 사용자 특성에 맞는 적절한 결과를 보여주며, 이것은 여러 사용자의 평가에 기반하는 현재 유투브 보다 더 좋은 만족감을 준다. A user usually gets the same suggesting results as everyone else in most of the multimedia social services, nowadays. To address the challenging problem of personalization in the social network, we propose a method which exploits user's activities, user's moods, and user's friend relationships from the social network to build a decision-making system. Depending on a current state of the user's mood, this system infers the most appropriated video for the user. In the system, the user evaluates a set of the given recommendation methods which extract from the user's database social network and assigns a vague value to each method by a weight. Then, we find the fuzzy collection solution for the system and classify the set of methods into subsets, and order the subsets based on its local dominance to choose the best appropriate method. Finally, we conduct an experiment using the YouTube API with a lot of video types. The experiment result shows that the channel recommendation system appropriately affords the user's character, it is more satisfying than the current YouTube based on an evaluation of several users.

IMPLEMENTASI METODE REST REQUEST PADA YOUTUBE WEB SERVICE UNTUK REPRESENTASI INFORMASI BERBASIS TIMELINE

Developing video search application using the service of a third party data (resources) provider is a complex process. Therefore to obtain the desired data, an efficient communication method is needed. This research explored the use of REST request communication method by using URL to identify video resources through YouTube API data. Video data that were identified will then be processed to get more relevant video search. Relevancy of the search result was determined by applying the mechanism of relevance-based system and user-based system. Based on the experiment analysis, it is concluded that key word has an important role in determining the relevance of the search result, as it is needed for timeline based representation.

The Development and Evaluation of a Network for Producing and Sharing Video Presentations

This paper describes the technology and methodology used in the development and evaluation of an online network to help instructors produce and share video presentations in a new and innovative way. The network offers an application and platform for recording and sharing video presentations. The application allows instructors to narrate and annotate PowerPoint slides and synchronize a variety of visual aids, including webcam footage, whiteboard, drawing board, and web browser. The platform uses YouTube API to upload and integrate videos with scrollable slide thumbnails. Quantitative and qualitative techniques were implemented to collect data and report on the usability of the network and effectiveness of video presentations. The results of usability evaluation showed that the network was perceived by instructors and students as effective, efficient, and relevance for self- production and sharing of video presentations. In addition, video presentations produced by participants met the criteria of quality of video presentations.