An Efficient Manifold Density Estimator for All Recommendation Systems

Most current neural recommender systems for session-based data cast recommendations as a sequential or graph traversal problem, applying recurrent networks (LSTM/GRU) or graph neural networks (GNN). This makes the systems increasingly elaborate in order to model complex user/item connection networks and results in poor scalability to large item spaces and long item view/click sequences. Instead on focusing on the sequential nature of session-based recommendation, we propose to cast it as a density estimation problem on item sets. We introduce EMDE (Efficient Manifold Density Estimator) - a method utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds using compressed representations we call sketches. Within EMDE, session behaviors are represented with weighted item sets, largely simplifying the sequential aspect of the problem. Applying EMDE to both top-k and session-based recommendation settings, we establish new state-of-the-art results on multiple open datasets in both uni-modal and multi-modal settings. EMDE has also been applied to many other tasks and areas in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We release the code at https://github.com/emde-conf/emde-session-rec.