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.
@article{arxiv.2006.09979,
title = {I know why you like this movie: Interpretable Efficient Multimodal Recommender},
author = {Barbara Rychalska and Dominika Basaj and Jacek Dąbrowski and Michał Daniluk},
journal= {arXiv preprint arXiv:2006.09979},
year = {2020}
}