RoFiGR improves item recommendations by refining coarse routing with collaborative signals
RoFiGR: Coarse Routing, Fine Refinement via Level-Adaptive Distillation in Multimodal Generative Recommendation
Information Retrieval
Summary
Recommendation systems try to suggest items by predicting codes that represent each item step by step. Making mistakes early on can exclude good choices later. The authors present RoFiGR, which uses a two-stage approach: it first makes broad category decisions using information about user preferences and item similarities, then refines predictions carefully. This method improves recommendation quality on datasets like Instruments, Arts, and Games. The authors also show how training and decoding strategies affect the performance.
What this means in practice
- •For e-commerce platform developers: Enhance product recommendation accuracy by integrating collaborative signals into sequential item code generation to reduce early-stage filtering errors.$Commercial implications: Enables improved personalized product suggestions that can increase sales and user engagement on online retail websites.
- •For digital media recommendation teams: Improve item ranking for arts or game recommendations by using multi-level routing with explicit item similarity encoding during generation.
Authors
Ji Dai, Zhipeng Wei, Zhuang Zhuang, Rongfeng Guo, Jie Chen, Quan Fang, Fei Pan
Abstract
Generative recommenders predict an item's semantic identifier (SID) one code at a time. Early codes select broad branches, so a routing error can exclude relevant items before fine-grained prediction begins. In multimodal recommendation, these coarse decisions must reflect collaborative preferences as well as item semantics. We introduce RoFiGR, a two-tree recommender that uses collaborative information in both SID construction and generation. It aligns cumulative L1-L2 code vectors with frozen collaborative item embeddings and explicitly encodes item distance and source level in the serialized history. During generation, a sequential teacher supervises legal branches. Fixed coarse-level weights maintain routing supervision, while a bounded adaptive residual allocates additional weight according to teacher-student demand. On Instruments, Arts, and Games, RoFiGR improves all five ranking metrics over the evaluated external baselines. Five-seed paired tests with Holm correction support the main NDCG@10 improvements. Comparisons with alternative collaborative supervision objectives and tokenizers support the proposed design. Matched task and decoding analyses distinguish the effects of training exposure, stream fusion, and candidate budgets.