New Recommendation Algorithm for Implicit Data Motivated by the Multivariate Normal Distribution

TL;DR

This paper introduces a novel recommendation algorithm for implicit data, inspired by the Multivariate Normal Distribution, which improves accuracy and simplicity by using only known interactions for predictions.

Contribution

The paper proposes a new MVN-inspired algorithm for implicit data that outperforms baselines and simplifies model complexity by relying solely on known interactions.

Findings

Matches or outperforms baseline accuracy

Simpler models with fewer hyperparameters

Effective for Top-N recommendation with small seed sizes

Abstract

The goal of recommender systems is to help users find useful items from a large catalog of items by producing a list of item recommendations for every user. Data sets based on implicit data collection have a number of special characteristics. The user and item interaction matrix is often complete, i.e. every user and item pair has an interaction value or zero for no interaction, and the goal is to rank the items for every user. This study presents a simple new algorithm for implicit data that matches or outperforms baselines in accuracy. The algorithm can be motivated intuitively by the Multivariate Normal Distribution (MVN), where have a closed form expression for the ranking of non-interactions given user's interactions. The main difference to kNN and SVD baselines is that predictions are carried out using only the known interactions. Modified baselines with this trick have a better accuracy, however it also results in simpler models with fewer hyperparameters for implicit data. Our results suggest that this idea should used in Top-N recommendation with small seed sizes and the MVN is a simple way to do so.