Video Recommendation

Abstract: Today�s generation unlike the previous ones prefer the content with more multimedia and less of texts and as a result of which there is a enormous growth in services including multimedia and the huge amount of video contents that is offered in online social networks. Due to this huge variety and numbers available, the netizens have great trouble in getting their desired content. Therefore, a lot of personalized content recommendation systems have been suggested to fulfil the needs of all the individuals. However, the proposed systems tend to ignore the fact this social multimedia content data has led to the big data era and that�s where big data comes into picture, which has greatly obstructed the process of video recommendation. In addition, none of the proposed systems consider both the privacy of user data (e,g., personal details, social status, ages and hobbies) and video service provider database (where all the videos are stored), which are extremely sensitive and of very significant value both content wise and commercially. To handle these problems, we propose a video recommendation system based on cloud assistance and big data and also on distributed online learning. In our proposal, service vendors are mapped onto as distributed cooperative learners who recommends videos according to user�s context and liking, while simultaneously also adapts to the video-selection strategy which is based on number of user-clicks count to increase number of feedbacks or rewards. Keywords: recommender system; data privacy; big data; provider database;distributed learning.