Sparse linear methods (SLIM) and their variations show outstanding performance, but they are memory-intensive and hard to scale. ImplicitSLIM improves embedding-based models by extracting embeddings from SLIM-like models in a computationally cheap and memory-efficient way, without explicit learning of heavy SLIM-like models. We show that ImplicitSLIM improves performance and speeds up convergence for both state of the art and classical collaborative filtering methods. Learnable embeddings are a core part of many collaborative filtering (CF) models. In this work, we propose an approach able to improve a wide variety of collaborative filtering models with learnable embeddings. Item-item methods, including kNN-based approaches (Sarwar et al., 2001) and sparse linear methods (SLIM) (Ning & Karypis, 2011), are making predictions based on item-item similarity. Previous research shows that the item-item weight matrix learned by SLIM-like models can become a part of other collaborative filtering models; e.g., RecWalk uses it as a transition probability matrix (Nikolakopoulos & Karypis, 2019). In this work, we reuse the item-item weight matrix in order to enrich embedding-based models with information on item-item interactions. Another motivation for our approach stems from nonlinear dimensionality reduction methods (e.g., VAEs) applied to collaborative filtering (Shenbin et al., 2020). We consider a group of manifold learning methods that aim to preserve the structure of data in the embedding space, that is, they force embeddings of similar objects to be similar.