Leveraging Large Language Models (LLMs) for recommendation has recently garnered considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the cost of fine-tuning LLMs on rapidly expanding recommendation data limits their practical application. To address this challenge, few-shot fine-tuning offers a promising approach to quickly adapt LLMs to new recommendation data. We propose the task of data pruning for efficient LLM-based recommendation, aimed at identifying representative samples tailored for LLMs' few-shot fine-tuning. While coreset selection is closely related to the proposed task, existing coreset selection methods often rely on suboptimal heuristic metrics or entail costly optimization on large-scale recommendation data. To tackle these issues, we introduce two primary objectives for the data pruning task in the context of LLM-based recommendation: 1) high accuracy aims to identify the influential samples that can lead to high overall performance; and 2) high efficiency underlines the low costs of the data pruning process. To pursue the two objectives, we propose a novel data pruning method incorporating two scores, namely influence score and effort score, to efficiently identify the influential samples. Particularly, the influence score is introduced to accurately estimate the influence of removing each sample on the overall performance. To achieve low costs of the data pruning process, we employ a small-sized surrogate model to replace LLMs to obtain the influence score. Considering the potential gap between the surrogate model and LLMs, we further propose an effort score to prioritize some hard samples specifically for LLMs. We instantiate the proposed method on two competitive LLM-based recommender models, and empirical results on three real-world datasets validate the effectiveness of our proposed method. In particular, our method uses only 2% samples to surpass the full data fine-tuning, reducing time costs by 97%.

Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook-based identifiers, exhibit shortcomings in encoding semantic information, incorporating collaborative signals, or handling code assignment bias. To address these limitations, we propose LETTER (a LEarnable Tokenizer for generaTivE Recommendation), which integrates hierarchical semantics, collaborative signals, and code assignment diversity to satisfy the essential requirements of identifiers. LETTER incorporates Residual Quantized VAE for semantic regularization, a contrastive alignment loss for collaborative regularization, and a diversity loss to mitigate code assignment bias. We instantiate LETTER on two models and propose a ranking-guided generation loss to augment their ranking ability theoretically. Experiments on three datasets validate the superiority of LETTER, advancing the state-of-the-art in the field of LLM-based generative recommendation.

Recent years have witnessed a flourishing of community-driven question answering (cQA), like Yahoo! Answers and AnswerBag, where people can seek precise information. After 2010, some novel cQA systems, including Quora and Zhihu, gained momentum. Besides interactions, the latter enables users to label the questions with topic tags that highlight the key points conveyed in the questions. In this article, we shed light on automatically annotating a newly posted question with topic tags that are predefined and preorganized into a directed acyclic graph. To accomplish this task, we present an end-to-end deep interactive embedding model to jointly learn the embeddings of questions and topics by projecting them into the same space for a similarity measure. In particular, we first learn the embeddings of questions and topic tags by two deep parallel models. Thereinto, we regularize the embeddings of topic tags via fully exploring their hierarchical structures, which is able to alleviate the problem of imbalanced topic distribution. Thereafter, we interact each question embedding with the topic tag matrix, i.e., all the topic tag embeddings. Following that, a sigmoid cross-entropy loss is appended to reward the positive question-topic pairs and penalize the negative ones. To justify our model, we have conducted extensive experiments on an unprecedented large-scale social QA dataset obtained from Zhihu.com, and the experimental results demonstrate that our model achieves superior performance to several state-of-the-art baselines.

Generative retrieval stands out as a promising new paradigm in text retrieval that aims to generate identifier strings of relevant passages as the retrieval target. This generative paradigm taps into powerful generative language models, distinct from traditional sparse or dense retrieval methods. However, only learning to generate is insufficient for generative retrieval. Generative retrieval learns to generate identifiers of relevant passages as an intermediate goal and then converts predicted identifiers into the final passage rank list. The disconnect between the learning objective of autoregressive models and the desired passage ranking target leads to a learning gap. To bridge this gap, we propose a learning-to-rank framework for generative retrieval, dubbed LTRGR. LTRGR enables generative retrieval to learn to rank passages directly, optimizing the autoregressive model toward the final passage ranking target via a rank loss. This framework only requires an additional learning-to-rank training phase to enhance current generative retrieval systems and does not add any burden to the inference stage. We conducted experiments on three public benchmarks, and the results demonstrate that LTRGR achieves state-of-the-art performance among generative retrieval methods. The code and checkpoints are released at https://github.com/liyongqi67/LTRGR.