# Personalised novel and explainable matrix factorisation

## TL;DR

This paper introduces NEMF, a matrix factorisation-based recommendation model that balances accuracy, novelty, and explainability, supported by a new explainability metric and user study insights.

## Contribution

The paper presents NEMF, a novel model that incorporates trade-offs between accuracy, novelty, and explainability in recommendations.

## Key findings

- NEMF achieves high accuracy while enhancing novelty and explainability.
- A new nDCG-based explainability metric effectively distinguishes explainability levels.
- User study confirms positive perception of explanations and attributes.

## Abstract

Recommendation systems personalise suggestions to individuals to help them in their decision making and exploration tasks. In the ideal case, these recommendations, besides of being accurate, should also be novel and explainable. However, up to now most platforms fail to provide both, novel recommendations that advance users' exploration along with explanations to make their reasoning more transparent to them. For instance, a well-known recommendation algorithm, such as matrix factorisation (MF), optimises only the accuracy criterion, while disregarding other quality criteria such as the explainability or the novelty, of recommended items. In this paper, to the best of our knowledge, we propose a new model, denoted as NEMF, that allows to trade-off the MF performance with respect to the criteria of novelty and explainability, while only minimally compromising on accuracy. In addition, we recommend a new explainability metric based on nDCG, which distinguishes a more explainable item from a less explainable item. An initial user study indicates how users perceive the different attributes of these "user" style explanations and our extensive experimental results demonstrate that we attain high accuracy by recommending also novel and explainable items.