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Momentum-based Gradient Methods in Multi-Objective Recommendation
- Publication Year :
- 2020
- Publisher :
- arXiv, 2020.
-
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.<br />Comment: 10 pages, 2 figures, 2 tables, accepted at RecSys 2021 - Workshop on Multi-Objective Recommender Systems (MORS)
- Subjects :
- FOS: Computer and information sciences
Computer Science - Machine Learning
Artificial Intelligence (cs.AI)
Computer Science - Artificial Intelligence
Statistics - Machine Learning
MathematicsofComputing_NUMERICALANALYSIS
Machine Learning (stat.ML)
Information Retrieval (cs.IR)
Computer Science - Information Retrieval
Machine Learning (cs.LG)
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....aa13aeb8f9cfc843ddf1c463c02e5022
- Full Text :
- https://doi.org/10.48550/arxiv.2009.04695