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Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees.

In this paper, we propose a general framework for generating Pareto efficient recommendations. Assuming that there are formal differentiable formulations for the objectives, we coordinate these objectives with a weighted aggregation. Then we propose a condition ensuring Pareto efficiency theoretically and a two-step Pareto efficient optimization algorithm. Meanwhile the algorithm can be easily adapted for Pareto Frontier generation and fair recommendation selection. We specifically apply the …