Summary
Predicting the next product a person will want is often done by having AI generate text descriptions, but this doesn’t match well with the actual task of picking top items from a big list. The authors created a system called TATK that improves this by using knowledge about how items relate to each other and by learning to rank the top choices better. They tested it on Amazon product reviews for musical instruments, CDs, vinyl, and video games, showing better accuracy than other approaches while keeping predictions fast. Their analysis shows that the method works best when there is good structured information about the items available.
What this means in practice
- •For ecommerce platform engineers: Improve product recommendation quality by integrating item relationships and ranking training guided by metadata in next-item prediction systems.
- •For streaming service recommender teams: Increase accuracy of sequential recommendations for media catalogs by applying knowledge-grounded verification to candidate lists generated by large language models.
Authors
Yuchen Guan, Jiaye Liu, Yifei Han, Zhenxi Zhang, Yixuan Weng, Bin Li
Abstract
LLM-based sequential recommenders usually cast next-item prediction as text generation, but this interface is poorly matched to full-catalog top-K ranking. We propose TATK, a Triple-Aware framework that couples Top-K Learning (TKL) with Knowledge-Grounded Verification (KGV) for LLM-based sequential recommendation. Top-K Learning combines context-aware metadata-KG prompt grounding with position-aware top-K rewards, aligning training with ranking utility; Knowledge-Grounded Verification then applies structure-aware reranking over the top-M candidates after a single LLM forward pass, using the same metadata-derived item graph. We evaluate TATK on Musical Instruments, CDs and Vinyl, and Video Games from Amazon Reviews 2023 under a matched R2ec-style full-catalog protocol. Experiments use Gemma-2-2B-It and Qwen2.5-3B-Instruct backbones, compare against sequential, generative, KG-augmented, and reasoning-enhanced baselines, and include component, reward-shape, sequence-perturbation, reranking, relation-quality, and candidate-pool diagnostics. TATK improves over the matched R2ec reproduction on all 36 reported metrics. On NDCG@10, it improves Qwen by 8.05%, 4.26%, and 3.78% on the three datasets, and improves Gemma by 27.03%, 10.52%, and 10.23%, while keeping inference within 1.17x of Base RecPO latency. The diagnostics show that structural evidence is most useful for recoverable top-M candidates with reliable KG support, and should be gated when metadata relations are sparse or noisy.