Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained large language models (LLMs) can provide rich semantic representations, existing approaches only rely on static encoding of fixed attributes, overlooking the crucial role of target audiences in defining item identity. Moreover, the semantic space struggles to reflect actual user behavior, resulting in a significant gap between semantic representations and behavioral patterns. To address these limitations, we propose GenAIR, a general framework that empowers sequential recommendation with Generative Archetype-grounded Item Representations. Specifically, we first leverage an LLM to analyze item metadata and infer textual description of the Archetype, which represents the conceptual profile of the item's ideal target audience. We then extract the corresponding embeddings in a single forward pass. Further, to ground these generative archetypes in real-world behavior, we introduce a behavioral calibration objective, which explicitly incorporates behavioral signals from actual interactions. This objective adjusts the structure of the embedding space to reflect empirical patterns. GenAIR enables seamless integration with most existing models while maintaining high efficiency. Comprehensive experiments conducted on three real-world datasets demonstrate that GenAIR significantly improves the performance of various sequential recommendation models and consistently outperforms state-of-the-art baseline approaches. Implementation codes are available at https://github.com/AI-Santiago/GenAIR.

Conditional Knowledge Graphs (CKGs) extend traditional knowledge graphs by incorporating conditional constraints, enabling a more accurate understanding of complex knowledge with conditional constraints for the semantic web. Conditional information extraction (CIE) aims to extract not only traditional fact triples but also their corresponding conditional qualifiers, forming quintuples that represents these constraints. Existing CIE methods typically treat conditional quintuples as flat structures, overlooking the hierarchical dependencies. Additionally, they often require exploring all possible mention combinations, leading to a large interaction space. These two issues hinder the extraction performance. To this end, we propose a Diffusion Model on Fact-condition Star Graph for CIE (Diff-CIE). We adapt a star graph structure where fact triples serve as central nodes and conditional tuples as leaf nodes, explicitly modeling the hierarchical dependencies. We then leverage the diffusion model to reformulate CIE as a progressive denoising process on these nodes, refining a fixed number of noised nodes into quintuples, thereby reducing the interaction space. Furthermore, to mitigate the inherent optimization instability in traditional diffusion-based information extraction methods, we introduce a deterministic in-order matching strategy to provide an auxiliary constraint. Extensive experiments on three datasets demonstrate that Diff-CIE consistently outperforms state-of-the-art baselines and has higher efficiency, achieving an improvement in F1 metric of over 1.19%, validating the effectiveness of our methods.

Core-set selection (CS) for deep learning has become crucial for enhancing training efficiency and understanding datasets by identifying the most informative subsets. However, most existing methods rely on heuristics or complex optimization, struggling to balance efficiency and effectiveness. To address this, we propose a novel CS objective that adaptively balances losses between core-set and non-core-set samples by minimizing the sum of squared losses across all samples. Building on this objective, we introduce the Maximum Reduction as Maximum Contribution criterion (MRMC), which identifies samples with the maximal reduction in loss as those making the maximal contribution to overall convergence. Additionally, a balance constraint is incorporated to ensure an even distribution of contributions from the core-set. Experimental results demonstrate that MRMC improves training efficiency significantly while preserving model performance with minimal cost.

Domain generalization (DG) tasks aim to learn cross-domain models from source domains and apply them to unknown target domains. Recent research has demonstrated that diverse and rich source domain samples can enhance domain generalization capability. This work argues that the impact of each sample on the model's generalization ability varies. Even a small-scale but high-quality dataset can achieve a notable level of generalization. Motivated by this, we propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in DG. First, we analyze that the objective of DG tasks is to maximize the inter-class distance within the same domain and minimize the intra-class distance across different domains. We design a domain adversarial selection method that prioritizes challenging samples in an active learning (AL) framework. Second, we hypothesize that even in a converged model, some feature subsets lack discriminatory power within each domain. We develop a method to identify and optimize these feature subsets, thereby maximizing inter-class distance of features. Lastly, We experimentally compare our DAAL algorithm with various DG and AL algorithms across four datasets. The results demonstrate that the DAAL algorithm can achieve strong generalization ability with fewer data resources, thereby significantly reducing data annotation costs in DG tasks.

The widespread adoption of location-based applications has created a growing demand for point-of-interest (POI) recommendation, which aims to predict a user’s next POI based on their historical check-in data and current location. However, existing methods often struggle to capture the intricate relationships within check-in data. This is largely due to their limitations in representing temporal and spatial information and underutilizing rich semantic features. While large language models (LLMs) offer powerful semantic comprehension to solve them, they are limited by hallucination and the inability to incorporate global collaborative information. To address these issues, we propose a novel method SeCor, which treats POI recommendation as a multi-modal task and integrates semantic and collaborative representations to form an efficient hybrid encoding. SeCor first employs a basic collaborative filtering model to mine interaction features. These embeddings, as one modal information, are fed into LLM to align with semantic representation, leading to efficient hybrid embeddings. To mitigate the hallucination, SeCor recommends based on the hybrid embeddings rather than directly using the LLM’s output text. Extensive experiments on three public real-world datasets show that SeCor outperforms all baselines, achieving improved recommendation performance by effectively integrating collaborative and semantic information through LLMs.

There is a natural correspondence between mathematical proofs and computer programs. For instance, a recursive function and its correctness relate directly to inductive proofs in mathematics. However, many undergraduate students feel a disconnect between mathematics and computer science. There are several proof assistant tools which have been used by the educational community to introduce such concepts to students, but since these tools are not primarily created for educational purposes, students often do not benefit from them to the expected extent.