Multi-Gradient Descent for Multi-Objective Recommender Systems

TL;DR

This paper introduces MGDRec, a stochastic multi-gradient descent method for multi-objective recommender systems, effectively optimizing diverse and uncorrelated objectives while outperforming existing approaches.

Contribution

It presents a novel stochastic multi-gradient descent algorithm that handles multiple, diverse objectives in recommender systems, including uncorrelated and differently scaled goals.

Findings

Outperforms state-of-the-art methods in traditional objective mixtures.

Enables combining objectives with different scales through gradient normalization.

Improves uncorrelated objectives like quality proportion alongside accuracy.

Abstract

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem. We show that this exceeds state-of-the-art methods in traditional objective mixtures, like revenue and recall. Not only that, but through gradient normalization we can combine fundamentally different objectives, having diverse scales, into a single coherent framework. We show that uncorrelated objectives, like the proportion of quality products, can be improved alongside accuracy. Through the use of stochasticity, we avoid the pitfalls of calculating full gradients and provide a clear setting for its applicability.