Momentum-based Gradient Methods in Multi-Objective Recommendation

• URL: http://arxiv.org/abs/2009.04695v3
• Date: Wed, 1 Sep 2021 16:47:26 GMT
• Title: Momentum-based Gradient Methods in Multi-Objective Recommendation
• Authors: Blagoj Mitrevski, Milena Filipovic, Diego Antognini, Emma Lejal Glaude, Boi Faltings, Claudiu Musat
• Abstract summary: We create a multi-objective model-agnostic Adamize method for solving single-objective problems. We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or conflicting.
• Score: 30.894950420437926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-gradient~descent usually relies on the combination of the gradients, not including the computation of first and second moments of the gradients. This leads to a brittle behavior and misses important areas in the solution space. In this work, we create a multi-objective model-agnostic Adamize method that leverages the benefits of the Adam optimizer in single-objective problems. This corrects and stabilizes~the~gradients of every objective before calculating a common gradient descent vector that optimizes all the objectives simultaneously. We evaluate the benefits of Multi-objective Adamize on two multi-objective recommender systems and for three different objective combinations, both correlated or conflicting. We report significant improvements, measured with three different Pareto front metrics: hypervolume, coverage, and spacing. Finally, we show that the \textit{Adamized} Pareto front strictly dominates the previous one on multiple objective pairs.

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