A new challenge on video recommendation by content

Abstract

When it comes to searching online, massive information is available, it is really hard to provide relevant information to users based on their interest. Although while searching for data based on user inputs, they need to search the entire database, which is also very frustrating and time-consuming. Video consumption becoming essential in most users' life. On the most video platforms, users get their recommended videos based on some algorithms, calculations, implicit feed-backs, watch, search behaviors and search history. New videos suffer from cold-start which happens to freshly uploaded videos in which no data or reviews are available. Therefore, it is not easy to recommend these videos to some users. Another real problem that users face every day is that finding the desired content depends on the video being labeled or has multiple views. The search engine will find the videos based on keywords or tags, not on the content inside the video. One of the solutions for this problem is recommending videos based on content. This paper presents a new challenge on proposing a video recommendation system based on content using objects and features with the ability to search or block specific scenes.