IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

TL;DR

This paper introduces techniques for content-based video relevance prediction to address the cold start problem in video recommendation systems, leveraging feature extraction and ensemble strategies to improve similarity predictions.

Contribution

It presents novel architectures and ensemble methods for predicting video relevance without metadata, specifically for the CBVRP challenge hosted by Hulu.

Findings

Encouraging prediction accuracy results

Effective use of frame and video level features

Potential to advance multimedia video recommendation research

Abstract

Internet has brought about a tremendous increase in content of all forms and, in that, video content constitutes the major backbone of the total content being published as well as watched. Thus it becomes imperative for video recommendation engines such as Hulu to look for novel and innovative ways to recommend the newly added videos to their users. However, the problem with new videos is that they lack any sort of metadata and user interaction so as to be able to rate the videos for the consumers. To this effect, this paper introduces the several techniques we develop for the Content Based Video Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM Multimedia Conference 2018. We employ different architectures on the CBVRP dataset to make use of the provided frame and video level features and generate predictions of videos that are similar to the other videos. We also implement several ensemble strategies to explore complementarity between both the types of provided features. The obtained results are encouraging and will impel the boundaries of research for multimedia based video recommendation systems.