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Internet has brought about a tremendous increase in content of all forms and,in that, video content constitutes the major backbone of the total contentbeing published as well as watched. Thus it becomes imperative for videorecommendation engines such as Hulu to look for novel and innovative ways torecommend the newly added videos to their users. However, the problem with newvideos is that they lack any sort of metadata and user interaction so as to beable to rate the videos for the consumers. To this effect, this paperintroduces the several techniques we develop for the Content Based VideoRelevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACMMultimedia Conference 2018. We employ different architectures on the CBVRPdataset to make use of the provided frame and video level features and generatepredictions of videos that are similar to the other videos. We also implementseveral ensemble strategies to explore complementarity between both the typesof provided features. The obtained results are encouraging and will impel theboundaries of research for multimedia based video recommendation systems.

IceBreaker: Solving Cold Start Problem for Video Recommendation Engines