I know why you like this movie: Interpretable Efficient Multimodal Recommender

Understand

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.