Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ supervised fine-tuning (SFT) to adapt LLMs to recommendation scenarios, and utilize beam search during inference to efficiently retrieve B top-ranked recommended items. However, we identify a critical training-inference inconsistency: while SFT optimizes the overall probability of positive items, it does not guarantee that such items will be retrieved by beam search even if they possess high overall probabilities. Due to the greedy pruning mechanism, beam search can prematurely discard a positive item once its prefix probability is insufficient.

Large Language Models (LLMs) have achieved rapid progress and are increasingly deployed in real-world applications such as digital assistants, education, healthcare, and recommendation. This deployment has driven growing interest in LLM personalization, which seeks to align model behavior with individual preferences and evolving contexts. Despite the rapid development of this area, existing research remains scattered, and a systematic tutorial dedicated to LLM personalization is still lacking. This tutorial presents a unified technical framework for LLM personalization, organized around five core dimensions: user memory, personalization architecture, alignment and post-training, inference-time adaptation, and deployment. We show how these components transform LLMs from generic response generators into user-adaptive systems, enabling structured user representation, memory integration, personalized optimization objectives, and context-aware reasoning throughout the model lifecycle. We further discuss key challenges, including lifelong learning, preference drift, privacy-preserving adaptation, trustworthiness, and evaluation under dynamic user distributions. By consolidating recent advances, this tutorial aims to equip participants with a comprehensive and principled understanding of LLM personalization and to inspire continued innovation in this rapidly evolving field.

Sequential recommenders generate recommendations based on users’ historical interaction sequences. However, in practice, these sequences are often contaminated by noisy interactions, which can arise from various factors such as clickbait, the influence of prominently positioned items, or accidental interactions. Such noise can significantly degrade recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, offer a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation presents notable challenges: (1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; (2) Fine-tuning on the denoising task can partially mitigate the issue of generating nonsensical outputs. However, even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucination issue of LLMs. To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs’ capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing corrected sequences to be flexibly applied across various recommendation models. To the best of our knowledge, this is the first work that employs LLMs for sequential recommendation denoising while addressing the unique challenges of adapting LLMs to this task. Extensive experiments conducted on three real-world datasets across two noise settings validate the effectiveness of LLM4DSR, demonstrating an average improvement of 12.9% in NDCG@20. The code is available at https://github.com/WANGBohaO-jpg/LLM4DSR .

Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS).Given the fundamental gap between the mechanism of LLMs and the requirement of RS, researchers have focused on fine-tuning LLMs with recommendationspecific data to enhance their performance.Language Modeling Loss (LML), originally designed for language generation tasks, is commonly adopted.However, we identify two critical limitations of LML: 1) it exhibits significant divergence from the recommendation objective; 2) it erroneously treats all fictitious item descriptions as negative samples, introducing misleading training signals.To address these limitations, we propose a novel Masked Softmax Loss (MSL) tailored for fine-tuning LLMs on recommendation.MSL improves LML by identifying and masking invalid tokens that could lead to fictitious item descriptions during loss computation.This strategy can effectively avoid the interference from erroneous

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher’s knowledge may not always be reliable; 2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher’s knowledge; 3) divergence in semantic space poses a challenge to distill the knowledge from embeddings.

In recommender systems, most graph-based methods focus on positive user feedback, while overlooking the valuable negative feedback. Integrating both positive and negative feedback to form a signed graph can lead to a more comprehensive understanding of user preferences. However, the existing efforts to incorporate both types of feedback are sparse and face two main limitations: 1) They process positive and negative feedback separately, which fails to holistically leverage the collaborative information within the signed graph; 2) They rely on MLPs or GNNs for information extraction from negative feedback, which may not be effective. To overcome these limitations, we introduceSIGformer, a new method that employs the transformer architecture to sign-aware graph-based recommendation. SIGformer incorporates two innovative positional encodings that capture the spectral properties and path patterns of the signed graph, enabling the full exploitation of the entire graph. Our extensive experiments across five real-world datasets demonstrate the superiority of SIGformer over state-of-the-art methods. The code is available at https://github.com/StupidThree/SIGformer.

With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (\aka IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection in RS. Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO; 2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support. Notably, while DR-GNN involves complex optimization, it can be implemented easily and efficiently. Our extensive experiments validate the effectiveness of DR-GNN against three typical distribution shifts. The code is available at https://github.com/WANGBohaO-jpg/DR-GNN.