Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, architectural innovations, and data curation strategies; (2) post-training techniques, including supervised fine-tuning and reinforcement learning, which adapt foundational models to downstream tasks and enhance their alignment and safety; (3) utilization strategies, such as in-context learning, prompt engineering, and agentic reasoning, that optimize real-world deployment and enable effective interaction with external environments; and (4) evaluation methods, encompassing benchmarks for key ability dimensions such as core language capabilities, reasoning, and safety, which support comprehensive and reliable assessment of model performance. Additionally, we identify critical research issues, including those concerning theoretical foundations, efficient scaling, alignment, and agentic capability, and highlight the open challenges they present. By synthesizing state-of-the-art insights and emerging trends, this survey aims to provide a systematic and comprehensive framework for understanding the trajectory, current limitations, and future directions of LLM progress.

This is an erratum for the article “Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach” published in ACM Trans. Inf. Syst. 43, 5, Article 114 (July 2025), 37 pages.

While modern recommender systems are instrumental in navigating information abundance, they remain fundamentally limited by static user modeling and reactive decision-making paradigms. Current large language model (LLM)-based agents inherit these shortcomings through their overreliance on heuristic pattern matching, yielding recommendations prone to shallow correlation bias, limited causal inference, and brittleness in sparse-data scenarios. We introduce STARec, a slow-thinking augmented agent framework that endows recommender systems with autonomous deliberative reasoning capabilities. Each user is modeled as an agent with parallel cognitions: fast response for immediate interactions and slow reasoning that performs chain-of-thought rationales. To cultivate intrinsic slow thinking, we develop anchored reinforcement training-a two-stage paradigm combining structured knowledge distillation from advanced reasoning models with preference-aligned reward shaping. This hybrid approach scaffolds agents in acquiring foundational capabilities (preference summarization, rationale generation) while enabling dynamic policy adaptation through simulated feedback loops. Experiments on MovieLens 1M and Amazon CDs benchmarks demonstrate that STARec achieves substantial performance gains compared with state-of-the-art baselines, despite using only 0.4% of the full training data.

The goal of transferable sequential recommendation (TSR) is to improve the performance of sequential recommenders in multiple target domains leveraging knowledge transferred from source domains. Most existing transferable sequential recommenders rely on item modality information but pay insufficient attention to the significant differences across domains (i.e., distinctive user behavior characteristics) in recommender systems. Besides, they are typically built on specific pre-trained model structures, limiting their model generalization to various different downstream domains and sequential recommenders in practical applications. This paper aims to address these issues by exploring the potential of mixture-of-LoRAs based domain adaption and an ensemble of sequential recommenders for learning generalizable and transferable domain-adaptive item representations. We propose MoL-TSR, a pre-trained plug-in mixture of low-rank adaption model for TSR. Specifically, we first devise a mixture-ofLoRAs based domain projector to produce domain-adaptive item representations based on both item modality and user behavior information for recommendation, enhancing the item discrimination and domain transfer ability. Then, we pre-train an ensemble of sequential recommendation models with contrastive learning to enhance the model-aspect generalization. As a general pretrained model, the mixture-of-LoRAs based domain projector in our MoL-TSR is a plug-in item semantic representation learning module, which is convenient to migrate into other transferable sequential recommenders to enhance item semantics better suited for recommendation. Extensive experiments demonstrate the effectiveness and model generalization of MoL-TSR, promising a practical solution for learning generalizable and transferable item semantic representations in recommendation. Our code is available at https://github.com/RUCAIBox/MoL-TSR.

Recently, many efforts have been devoted to building effective sequential recommenders.Despite their effectiveness, these methods typically develop a single model to serve all users.However, our empirical studies reveal that different sequential encoders have intrinsic architectural biases and tend to focus on specific behavioral patterns, i.e., particular frequency range of user behavior sequences.For example, the Self-Attention module is essentially a low-pass filter, focusing on low-frequency information while neglecting the high-frequency details.This evidently limits their ability to capture diverse user patterns, leading to suboptimal recommendations.To tackle this problem, we present FamouSRec, a Frequency-Augmented Mixture-of-Heterogeneous-Experts Framework for personalized Recommendations.Our approach builds an MoEbased recommender system, integrating the strengths of various experts to achieve diversified user modeling.For developing the MoE framework, as the key to our approach, we instantiate experts with various model architectures, aiming to leverage their inherent architectural biases and capture diverse behavioral patterns.For selecting appropriate experts to serve individuals, we introduce a frequency-augmented router.It first identifies frequency components in user behavior sequences that are suited for expert encoding, and then conducts customized routing based on the informativeness of these components.Building on this framework, we further propose two novel contrastive tasks to enhance expert

Recently, Large Language Models (LLMs) such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender systems. To assess how effectively LLMs can be used in recommendation tasks, our study primarily focuses on employing LLMs as recommender systems through prompt engineering. We propose a general framework for leveraging LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. To conduct our analysis, we formalize the input of LLMs for recommendation into natural language prompts with two key aspects and explain how our framework can be generalized to various recommendation scenarios. As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs. As for prompt engineering, we further analyze the impact of four important components of prompts, i.e., task descriptions, user interest modeling, candidate items construction, and prompting strategies. In each section, we first define and categorize concepts in line with the existing literature. Then, we propose inspiring research questions followed by detailed experiments on two public datasets, in order to systematically analyze the impact of different factors on recommendation performance. Based on our empirical analysis, we finally summarize promising directions to shed lights on future research.

With the widespread deployment of recommender systems on various online platforms, researchers are striving to develop transferable recommendation algorithms that can effectively adapt to new task scenarios without requiring the re-training of new recommenders. However, there have been challenges in dealing with explicit ID modeling in this context. Recently, researchers have drawn inspiration from the achievements of pre-trained language models (PLMs), making it possible to acquire ID-agnostic representations by utilizing the corresponding texts of items. These representations have shown to be transferable across diverse domains. However, while these methods demonstrate generalization, they are less proficient in making personalized recommendations as they learn universal representation. In light of this issue, we present a review-enhanced universal sequence representation learning approach named RUNSRec . Our goal is to not only comprehend universal user behavioral patterns across different domains but also capture their inherent preferences to make recommendations. Our approach makes three technical advancements toward this objective. Firstly, we introduce a lightweight item encoding architecture based on parametric whitening and mixture-of-experts enhanced adapter. It learns discriminative item textual representations by encoding their corresponding identity text and review text, with a discriminative keyword extraction method to enhance the representation identifiability. Secondly, we propose a universal sequence representation learning method that enables the training of transferable recommenders across diverse domains, based on two novel contrastive learning tasks. Furthermore, we introduce a personalized adapter tuning mechanism that enables the universal recommender to capture user personal preferences in a parameter-efficient way. By incorporating universal behavioral patterns learned during the pre-training stage and personalized user tastes captured through adapter tuning, our approach achieves a better balance between generalization and personalization in transferable recommender systems. Extensive experiments conducted on five real-world datasets have demonstrated the effectiveness of our proposed approach.

In the past few decades, recommender systems have attracted much attention in both research and industry communities. Existing recommendation models mainly learn the underlying user preference from historical behavior data (typically in the forms of item IDs), and then estimate the user–item matching relationships for recommendations. Inspired by the recent progress on large language models (LLMs), we develop a different recommendation paradigm, considering recommendation as instruction following by LLMs. The key idea is that the needs of a user can be expressed in natural language descriptions (called instructions ), so that LLMs can understand and further execute the instruction for fulfilling the recommendation. For this purpose, we instruction tune the 3B Flan-T5-XL, to better adapt LLMs to recommender systems. We first design a general instruction format for describing the preference, intention, and task form of a user in natural language. Then we manually design 39 instruction templates and automatically generate large amounts of user-personalized instruction data with varying types of preferences and intentions. To demonstrate the effectiveness of our approach, we instantiate the instructions into several widely studied recommendation (or search) tasks, and conduct extensive experiments with real-world datasets. Experiment results show that our approach can outperform several competitive baselines, including the powerful GPT-3.5, on these evaluation tasks. Our approach sheds light on developing user-friendly recommender systems, in which users can freely communicate with the system and obtain accurate recommendations via natural language instructions.

Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket. However, most of existing works merely focus on the correlations between user preferences and predicted items, ignoring the essential correlations among items in the next basket, which often results in over-homogenization of predicted items. In this work, we presents a Generative next-basket Recommendation model (GenRec), a novel NBR paradigm that generates the recommended items one by one to form the next basket via an autoregressive decoder. This generative NBR paradigm contributes to capturing and considering item correlations inside each baskets in both training and serving. Moreover, we jointly consider user’s both item- and basket-level contextual information to better capture user’s multi-granularity preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model.

In recent years, the reproducibility of recommendation models has become a severe concern in recommender systems. In light of this challenge, we have previously released a unified, comprehensive and efficient recommendation library called RecBole, attracting much attention from the research community. With the increasing number of users, we have received a number of suggestions and update requests. This motivates us to make further improvements on our library, so as to meet the user requirements and contribute to the research community. In this paper, we present a significant update of RecBole, making it more user-friendly and easy-to-use as a comprehensive benchmark library for recommendation. More specifically, the highlights of this update are summarized as: (1) we include more benchmark models and datasets, improve the benchmark framework in terms of data processing, training and evaluation, and release reproducible configurations to benchmark the recommendation models; (2) we upgrade the user friendliness of our library by providing more detailed documentation and well-organized frequently asked questions, and (3) we propose several development guidelines for the open-source library developers. These extensions make it much easier to reproduce the benchmark results and stay up-to-date with the recent advances on recommender systems. Our update is released at the link: https://github.com/RUCAIBox/RecBole.