Chang Liu

Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically involves two key components: (1) optimizing the tokenizer to derive suitable item identifiers, and (2) training the recomm...

Knowledge Graphs (KGs) have proven highly effective for recommendation systems by capturing latent item relationships, while recent integration of Large Language Models (LLMs) has further enhanced semantic understanding and addressed knowledge sparsity issues. Nevertheless, current KG&LLM-based m...

Neural retrieval models typically rely on fixed, hand-crafted preprocessing pipelines designed independently of the retrieval task, leading to suboptimal performance that varies across datasets and architectures. We propose PrepRet, a framework that jointly optimizes preprocessing pipeline select...