Recommender Systems have become essential tools in any modern video-sharing platform. Although, recommender systems have shown to be effective in generating personalized suggestions in video-sharing platforms, however, they suffer from the so-called New Item problem. New item problem, as part of Cold Start problem, happens when a new item is added to the system catalogue and the recommender system has no or little data available for that new item. In such a case, the system may fail to meaningfully recommend the new item to the users.
In this paper, we propose a novel recommender system that is based on visual tags, i.e., tags that are automatically annotated to videos based on visual description of the videos. Such visual tags can be used in an extreme cold start situation, where neither any rating, nor any tag is available for the new video. The visual tags could also be used in the moderate cold start situation when the new video might have been annotated with few tags. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the video content.
We have used a large dataset of videos and shown that automatically extracted visual tags can be incorporated into the cold start recommendation process and achieve superior results compared to the recommendation based on human-annotated tags.