Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing

Nowadays, in online social networks, there is an instantaneous extension of multimedia services and there are huge offers of video contents which has hindered users to acquire their interests. To solve these problem different personalized recommendation systems had been suggested. Although, all the personalized recommendation system which have been suggested are not efficient and they have significantly retarded the video recommendation process. So to solve this difficulty, context extractor based video recommendation system on cloud has been proposed in this paper. Further to this the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead and recommendation process is done by considering the context details of the users, it also uses rule based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping and storing process.

A Short Video Recommendation System

With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favourite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, we propose a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Big Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, we improve the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high-performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system. Keywords: Short, video, recommendation , machine learning

A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition

The advertising market’s use of smartphones and kiosks for non-face-to-face ordering is growing. An advertising video recommender system is needed that continuously shows advertising videos that match a user’s taste and displays other advertising videos quickly for unwanted advertisements. However, it is difficult to make a recommender system to identify users’ dynamic preferences in real time. In this study, we propose an advertising video recommendation procedure based on computer vision and deep learning, which uses changes in users’ facial expressions captured at every moment. Facial expressions represent a user’s emotions toward advertisements. We can utilize facial expressions to find a user’s dynamic preferences. For such a purpose, a CNN-based prediction model was developed to predict ratings, and a SIFT algorithm-based similarity model was developed to search for users with similar preferences in real time. To evaluate the proposed recommendation procedure, we experimented with food advertising videos. The experimental results show that the proposed procedure is superior to benchmark systems such as a random recommendation, an average rating approach, and a typical collaborative filtering approach in recommending advertising videos to both existing users and new users. From these results, we conclude that facial expressions are a critical factor for advertising video recommendations and are helpful in properly addressing the new user problem in existing recommender systems.

THE ALGORITHM PLAYGROUND: A CONTENT ANALYSIS OF USER-PRODUCED CHILDREN’S VIDEOS ON YOUTUBE

With early-childhood mobile media device use on the rise, online video content plays an ever-increasing role in children’s lives. Of the wide variety of content available to children, user-produced videos on YouTube seem to be most popular. However, due to the platform’s size and the overwhelming number of child-targeted videos found on YouTube, scholars have been struggling with how to approach and study this topic. This study aims to address the gap in research by analyzing prevalent user-produced children’s videos on YouTube, with research questions focusing on video genres, their features, and content themes. Drawing on YouTube’s popularity-measurements and video recommendation algorithm, a corpus of 100 user-produced videos targeted to children was assembled. A content analysis of these videos led to the identification and conceptualization of 13 distinct genres of user-produced children’s videos: unboxing, surprise eggs, finger family, play-doh, nursery rhymes, kids songs, learning, pretend play (enactment), pretend play (toys), storytelling, arts & crafts, entertainer in character, and process repetition. Furthermore, the findings indicate that there are often unique interplays between genre type and the content, the production format, and the overall quality and educational rating. In addition to shedding light on the importance of studying child-targeted content on YouTube, this study’s main contribution is a typological map of the user-produced children’s video ecosystem that future studies from various fields can draw on.