Abstract: Globoplay is Globo Group’s digital video streaming platform and offers a very diverse video content catalogue ranging from international to brazilian productions such as movies, series, soap operas, and TV programs produced by Globo Group. One of the challenges with such large and diverse content collection is its distribution to the user base in order to help our subscribers with finding relevant content that meets their expectations and to increase their engagement with the product. In this work, we show the result of a content-based recommendation approach based on multi-modal features such as visual characteristics and audio patterns found in the video content. Using techniques applied to short videos, we model it as a similarity problem based on the content of the video, where, given a video, we establish the top-n videos most similar to it in the collection. For the evaluation, we conducted a study through interviews with a group of users to understand their perception of recommendations based on audiovisual characteristics. For the future, we plan to: explore and define the best approach to combine text, audio and video features for video recommendations; explore audiovisual features with other recommendation approaches such as session based and collaborative filtering; perform AB testing in production; and evaluate the proposal impact in business metrics.