PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking

Abstract

Multi-objective re-ranking has become a critical component of modern multi-stage recommender systems, as it tasked to balance multiple conflicting objectives such as accuracy, diversity, and fairness. Existing multi-objective re-ranking methods typically optimize aggregate objectives at the item level using static or handcrafted preference weights. This design overlooks that users inherently exhibit Pareto-optimal preferences at the intent level, reflecting personalized trade-offs among objectives rather than fixed weight combinations. Moreover, most approaches treat re-ranking task for each user as an isolated problem, and repeatedly learn the preferences from scratch. Such a paradigm not only incurs high computational cost, but also ignores the fact that users often share similar preference trade-off structures across objectives. Inspired by the existence of homogeneous multi-objective optimization spaces where Pareto-optimal patterns are transferable, we propose PreferRec, a novel framework that explicitly models and transfers Pareto preferences across users. Specifically, PreferRec is built upon three tightly coupled components: Preference-Aware Pareto Learning aims to capture user intrinsic trade-offs among multiple conflicting objectives at the intent level. By learning Pareto preference representations from re-ranking populations, this component explicitly models how users prioritize different objectives under diverse contexts. Knowledge-Guided Transfer facilitates efficient cross-user knowledge transfer by distilling shared optimization patterns across homogeneous optimization spaces. The transferred knowledge is then used to guide solution selection and personalized re-ranking, biasing the optimization process toward high-quality regions of the Pareto front while preserving user-specific preference characteristics.

Figures (9)

• Figure 1: Comparison of different multi-objective recommendation paradigms. The left and middle panels illustrate traditional accuracy-oriented Top-K sorting and linear trade-off based diversified re-ranking (e.g., MMR), respectively. The right panel depicts the proposed PreferRec framework.
• Figure 2: Overview of PreferRec. The framework integrates evolutionary multi-objective optimization with global knowledge transfer. Guided initialization generates initial solutions for each user, which are optimized by evolutionary search. During optimization, preference vectors are periodically constructed from current solutions and used to train a Pareto Learning Network. The learned global knowledge is directly mixed with user-specific solutions to guide subsequent search. Final recommendations are selected from the Pareto front under different trade-off preferences.
• Figure 3: Performance comparison of PreferRec instantiated with different base models (ComiRec, SASRec, GRU4Rec) against representative baselines in dataset ML-1M.
• Figure 4: Ablation study on Knowledge Transfer.
• Figure 5: Performance and training time under different knowledge transfer intervals $t$.