  Recently, the Efficient Manifold Density Estimator (EMDE) model has been
introduced. The model exploits Local Sensitive Hashing and Count-Min Sketch
algorithms, combining them with a neural network to achieve state-of-the-art
results on multiple recommender datasets. However, this model ingests a
compressed joint representation of all input items for each user/session, so
calculating attributions for separate items via gradient-based methods seems
not applicable. We prove that interpreting this model in a white-box setting is
possible thanks to the properties of EMDE item retrieval method. By exploiting
multimodal flexibility of this model, we obtain meaningful results showing the
influence of multiple modalities: text, categorical features, and images, on
movie recommendation output.
