IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

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

Smart City Development with Urban Transfer Learning

Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with limited data?'. To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city planners and relevant practitioners with guidelines on how to apply this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies in public safety, transportation management, etc. We also summarize a few research opportunities and expect this article can attract more researchers to study urban transfer learning