Recommendation systems will always stay relevant -- users want to see personalized content, the best of the catalog (in the case of our iFunny app -- trending memes and jokes). Our team is testing dozens of hypotheses on how a smart feed can improve user experience. This article will tell you how we implemented the second-ranking level of the model above the collaborative one: what difficulties we encountered, and how they affected the metrics. Usually, a matrix decomposition, such as implicit.ALS, is used to help improve the feed. In this method, for each user and each object, we get the embeddings, and the content, whose embeddings are the closest (in cosine measure) to the user's embeddings, ends up in the top recommendations.