The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models(LLMs) can model recommendation tasks as language analysis tasks and provide zero-shot results based on their vast open-world knowledge. However, the large scale of the item corpus poses a challenge to LLMs, leading to substantial token consumption that makes it impractical to deploy in real-world recommendation systems. To tackle this challenge, we introduce a tree-based LLM recommendation framework LLMTreeRec, which structures all items into an item tree to improve the efficiency of LLM’s item retrieval. LLMTreeRec achieves state-of-the-art performance under the system cold-start setting in two widely used datasets, which is even competitive with conventional deep recommendation systems that use substantial training data. Furthermore, LLMTreeRec outperforms the baseline model in the A/B test on Huawei industrial system. Consequently, LLMTreeRec demonstrates its effectiveness as an industry-friendly solution that has been successfully deployed online.

With the rapid development of Natural Language Understanding for information retrieval, fine-tuned deep language models, e.g., BERT-based, perform remarkably effective in passage searching tasks. To lower the architecture complexity, the recent state-of-the-art model ColBERT employs Contextualized Late Interaction paradigm to independently learn fine-grained query-passage representations. Apart from the architecture simplification, embedding binarization, as another promising branch in model compression, further specializes in the reduction of memory and computation overheads. In this concise paper, we propose an effective post-training embedding binarization approach over ColBERT, achieving both architecture-level and embedding-level optimization for online inference. The empirical results demonstrate the efficaciousness of our proposed approach, empowering it to perform online query-passage matching acceleration.