Improved Accuracy, Declining Orders: Uncovering the Vicious Competition Trap in Multi-Domain Recommendation

E-commerce recommendation platforms often operate across multiple domains, giving rise to Multi-Domain Recommendation (MDR) methods. These methods leverage auxiliary information from other domains to capture comprehensive user preferences and enhance recommendation accuracy. However, cross-domain knowledge transfer often causes domains to capture similar preferences. Additionally, most MDR models optimize for next-item prediction, which promotes greedy strategies focused on immediate gains within individual domains while neglecting the platform-wide profit. These two factors result in vicious competition, as our empirical analysis reveals: Domains recommend overlapping item lists, competing for the same pool of user attention and platform traffic, reducing the overall orders. To address this, we propose the Direct Preference Optimization from Manual Rules (DPOMR) training paradigm for MDR. Inspired by Reinforcement Learning from Human Feedback (RLHF), DPOMR learns user preferences from pairwise preference data generated through manual rules rooted in business expertise. Furthermore, we design a variant of Direct Preference Optimization (DPO) to bypass expensive RL training. Extensive experiments demonstrate the advantages of DPOMR, including: (1) achieving SOTA performance in boosting overall orders(2) showing compatibility with various backbones, and (3) offering robust extendibility to integrate diverse manual rules tailored to specific business needs.