Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems

TL;DR

This paper presents MultiTRON, a transformer-based approach that efficiently approximates the Pareto front in multi-objective session-based recommender systems, enabling tailored trade-offs between key metrics.

Contribution

It introduces a novel method that trains a single model to represent the entire Pareto front, allowing flexible multi-objective optimization in recommender systems.

Findings

Effective Pareto front approximation demonstrated

Model adapts to different stakeholder preferences

Outperforms baseline methods in offline and online tests

Abstract

This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural network. Our approach optimizes trade-offs between key metrics such as click-through and conversion rates by training on sampled preference vectors. A significant advantage is that after training, a single model can access the entire Pareto front, allowing it to be tailored to meet the specific requirements of different stakeholders by adjusting an additional input vector that weights the objectives. We validate the model's performance through extensive offline and online evaluation. For broader application and research, the source code is made available at https://github.com/otto-de/MultiTRON. The results confirm the model's ability to manage multiple recommendation objectives effectively, offering a flexible tool for diverse business needs.