This work is supported by National Natural Science Foundation of China (No. 62406057), the Fundamental Research Funds for the Central Universities (No. ZYGX2025XJ042), the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No. 2023ZD0501806), and the Sichuan Science and Technology Program (No. 2024ZDZX0011).
Modern information retrieval (IR) must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating vocabulary mismatch, but its design space has been reshaped by pre-trained and large language models (PLMs/LLMs). This survey reviews QE methods in the PLM/LLM era and provides a unified view of the emerging landscape. We first summarize how different model families enable new expansion behaviors, including stronger contextualization, more controllable generation, and instruction-following. We then organize recent techniques along four complementary design dimensions: where expansion is injected in the pipeline, how it is grounded and interacts with corpus evidence, how it is learned or aligned, and how structured knowledge, such as knowledge graphs, is incorporated. Beyond taxonomy, we synthesize application patterns and deployment considerations across representative retrieval settings, highlighting practical tradeoffs among effectiveness, controllability, grounding quality, and operating cost. Finally, we outline open challenges and future directions toward more reliable, safe, efficient, and continually adaptive QE under real-world constraints (resources are available at https://github.com/lmh0921/QueryExpansion-PLM-LLM-Survey-paperList).
Recently, generative aspect-level sentiment quadruple prediction (ASQP) methods based on pre-trained language models have made significant progress. However, some challenges remain in extracting and recognizing complex sentiment elements from semantically rich sentences, limiting the generalization and adaptability of unidirectional generative models in aspect-level sentiment analysis. To overcome this limitation, this article proposes a Generative Aspect-Based Sentiment Quadruple Prediction Model based on Multi-Order Prompting (GenMOP). The model draws on the concept of prompt learning and introduces a multi-order prompting strategy, which breaks the traditional framework of a single generative order and enhances the flexibility and adaptability of the model. Furthermore, we integrate a quadruple quantity-aware module and a multi-view uncertainty-aware module based on a basic generative architecture, not only providing the model with more fine-grained information about the quadruple quantity but also improving the prediction accuracy through uncertainty estimation. The extensive experiments show that the GenMOP method achieves excellent performance in the ASQP task. On the four benchmark datasets including Rest15, Rest16, Rest and Lap, our model achieves F1 score improvements of 1.26%, 0.23%, 0.28%, and 2.07%, respectively, compared to existing state-of-the-arts, demonstrating its effectiveness and superiority in dealing with the joint extraction of multiple sentiment elements of the ASQP model.
Collaborative Filtering (CF) methods commonly use negative sampling to improve preference learning by contrasting observed interactions with unobserved items. While effective, conventional practice implicitly treats all unclicked items as equally informative, regardless of their semantic group (e.g., genre or category). This overlooks a critical limitation: models may exploit coarse group distinctions rather than genuine finegrained preferences, thereby inducing exposure imbalances across item groups. Such imbalances constitute a violation of item-side fairness, which seeks equitable exposure and evaluation for items from different semantic groups. When negative samples are drawn predominantly from groups semantically distant from a user's positives, the learning signal becomes biased and comparisons unfair. We therefore revisit negative sampling through the lens of item-side fairness and argue that genuine fairness requires context-aware sampling that ensures like-for-like comparisons within each semantic group. To this end, we introduce FairNS, a diffusion-based sampling framework that generates negative samples within the same semantic group as the user's positives, encouraging fair intra-group contrasts that respect group integrity. By centering training on these intra-group comparisons, FairNS mitigates cross-group bias and enables the recommender to learn more precise user preferences. FairNS is optimized via a bi-level objective that jointly refines the sampling mechanism and the recommendation model. Experiments on three benchmark datasets show that FairNS achieves a favorable fairness-accuracy tradeoff.
The decentralized nature of federated recommender systems (FedRecs) introduces new security risks, particularly target item promotion attacks, where attackers may attempt to manipulate the ranking and exposure rate of target items in users’ recommendation results. However, existing attacks typically promote each target item broadly to all users, which will compromise attack stealthiness while yielding only marginal gains in effectiveness. To address these issues, we propose a novel pro xy- it em based t arget u ser a ttack method on FedRecs, called ProitTUA , which selectively promotes each target item to a relevant user group rather than all users. The key novelty is to associate each target item with a relevant user group via category-wise proxy items. ProitTUA consists of two key stages: (1) item latent category inference, which infers latent item categories by leveraging auxiliary domain modeling and attack domain adaptation; and (2) proxy-item-based attack construction, which identifies proxy items for each category and then aligns each target item's embedding with its corresponding proxies. Extensive experiments show ProitTUA achieves superior attack effectiveness and stealthiness. To mitigate the potential threat posed by ProitTUA, we further design a defense mechanism based on gradient dynamics analysis. Experimental results demonstrate its effectiveness in reducing ProitTUA's impact on FedRecs.
