Textual data is a key signal for modeling relationships between news articles in recommendation systems. However, exaggerated and biased texts often hinder the accurate understanding of textual content, negatively affecting recommendation quality. Multimodal approaches have been proposed to address this issue by incorporating associated news images, but they often overlook the complementary role of visual information. To bridge this gap, we propose an Image-Guided Knowledge (IGKnow) distillation framework that transfers image-guided debiasing capability from a multimodal LLM into a text-only LLM. This design allows the distilled model to extract factual, debiased triples from text alone while preserving the benefits of multimodal learning. To further enhance stability, we introduce Triple-level Supervised Fine-Tuning (TriSFT), a permutation-invariant training for triple structures. Moreover, we refine the model through preference alignment to ensure objective, coherent knowledge extraction across news histories. We evaluate the extracted knowledge in sequential news recommendation and show that debiased triples improve recommendation performance.

Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.

Joint information extraction aims to convert unstructured text into structured knowledge by identifying entities and their relations. However, existing methods often rely on static span formation and relation-agnostic validation, limiting their ability to capture dynamic, context-sensitive semantics. We present RePooL, a hierarchical validation framework that performs fine-grained token-level filtering followed by coarse-grained span-level validation, enabling robust multi-granular semantic modeling. RePooL constructs a dual-view knowledge graph that models tokens and relations as distinct node types. It leverages auxiliary structural relations to encode token-relation semantic compatibility via subject and object roles and to compose multi-token spans dynamically, thereby enabling relation-aware validation across multiple granularities. To further strengthen semantic grounding, RePooL incorporates LLM-guided alignment, which evaluates candidate triples against the input text to specifically reinforce coherent extractions. Extensive experiments on standard IE benchmarks show that RePooL achieves superior performance, demonstrating its effectiveness in modeling fine-grained entity-relation interactions.

Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph (KG) information into dialogue generation and recommendation remains a pressing issue. Traditional approaches typically combine KG information directly with dialogue content, which often struggles with complex semantic relationships, resulting in recommendations that may not align with user expectations.

Sequential recommendation has been receiving increasing attention from researchers. Existing sequential recommendation models leverage deep learning models to capture sequential features. However, these methods ignore confounders in the recommendation process, which can lead the model to learn incorrect correlations and fail to accurately capture users' true preferences. Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a P reference- a ware C ausal I ntervention and Counter f a c tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. Initially, we model the causal graph of sequential recommendation and categorize user preferences into global long-term preferences, local long-term preferences, and short-term preferences. Then, we introduce the front-door criterion to eliminate the interference of confounders and design different self-attention mechanisms to estimate the causal effects, aiming to capture users' true preferences. In addition, based on counterfactual thinking, we design a counterfactual data augmentation module to generate enriched sequences. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach over state-of-the-art sequential recommendation methods.

A large number of spatial knowledge graphs (SKGs) are available from spatially enriched knowledge bases, e.g., DBpedia and YAGO2. This provides a great chance to understand valuable information about the regions surrounding us. However, it is hard to comprehend SKGs due to the explosively growing volume and the complication of the graph structures. Thus we study the problem of similar region search (SRS), which is an easy-to-use but effective way to explore spatial data. The effectiveness of SRS highly depends on how to measure the region similarity. However, existing approaches cannot make use of the rich information contained in SKGs thus may lead to incorrect results. In this paper, we propose a spatial knowledge representation learning method for region similarity, namely SKRL4RS. SKRL4RS firstly encodes the spatial entities of an SKG into a vector space to make it easier to extract useful features. Then regions are represented by 3-D tensors using the spatial entity embeddings together with geographical information. Finally, region tensors are fed into the conventional triplet network to learn the feature vectors of regions. The region similarity measure learned by SKRL4RS can capture the hierarchical types, semantic relatedness, and relative locations of spatial entities inside a region. Experimental results on two real-world datasets show that our SKRL4RS outperforms the state-of-the-art by a significant margin in terms of the accuracy of measuring region similarity.

The conventional works on spatial keyword queries for a knowledge base focus on finding a subtree to cover all the query keywords. The retrieved subtree is rooted at a place vertex, spatially close to a query location and compact in terms of the query keywords. However, user requirements may not be satisfied by a single subtree in some application scenarios. A group of subtrees should be combined together to collectively cover the query keywords. In this paper, we propose and study a novel way of searching on a spatial knowledge, namely collective spatial keyword query on a knowledge base (CoSKQ-KB). We formalize the problem of CoSKQ-KB and design a baseline method for CoSKQ-KB (BCK). To further speed up the query processing, an improved scalable method for CoSKQ-KB (iSCK) is proposed based on a set of efficient pruning and early termination techniques. In addition, we conduct empirical experiments on two real-world datasets to show the efficiency and effectiveness of our proposed algorithms.