Rich user-item interactions are essential for building reliable recommender systems, as they reflect user preference patterns. However, minor language news recommendation platforms suffer from limited interactions due to a small user base. A natural solution is to apply well-established English recommender systems to minor language news recommendation, but the linguistic gap can lead to inaccurate modeling of minor language news content. Therefore, enabling few-shot minor language news recommender systems to capture both content information and preference patterns remains a challenge. Based on the observation that preference patterns are similar across languages, we propose a minor language news recommendation model by cross-lingual preference pattern transfer, named PPT. Our model adopts the widely used two-tower architecture and employs the large language model as the backbone of the news encoder. Through cross-lingual alignment, the strong English capability of the news encoder is extended to minor languages, thus enhancing news content representations. Additionally, through cross-lingual news augmentation, PPT simulates interactions of minor language news in the English domain, which facilitates the transfer of preference patterns from the many-shot English domain to the few-shot minor language domain. Extensive experiments on two real-world datasets across 15 minor languages demonstrate the superiority and generalization of our proposed PPT in addressing minor language news recommendation.

“隐式情感分析因其缺乏显式情感线索的特性是情感分析领域的重要研究难点之一。传统的隐式情感分析方法通常针对隐式情感文本本身的信息进行建模,没有考虑隐式情感的主观差异性特征。本文提出了一种基于异构用户知识融合的隐式情感分析模型HELENE,首先从用户数据中挖掘用户异构的内容知识、社会化属性知识以及社会化关系知识,异构用户知识融合学习框架基于图神经网络模型结合动态预训练模型分别从用户的内部信息和外部信息两个维度对其进行画像建模;在此基础上与隐式情感文本语义信息进行融合学习,使得模型可以对隐式情感进行主观差异化建模表示。此外,本文构建了一个用户个性化通用情感分析语料库,涵盖了较为完整的文本内容信息、用户社会化属性信息和关系信息,可同时满足面向用户个性化建模的隐式或显式情感分析相关研究任务的需要。在所构建数据集上的实验结果显示,本文的方法相比基线模型在用户个性化隐式情感分析任务上具有显著的提升效果。”

Text simplification is an important branch of natural language processing. At present, methods used to evaluate the semantic retention of text simplification are mostly based on string matching. We propose the SEMA (text Simplification Evaluation Measure through Semantic Alignment), which is based on semantic alignment. Semantic alignments include complete alignment, partial alignment and hyponymy alignment. Our experiments show that the evaluation results of SEMA have a high consistency with human evaluation for the simplified corpus of Chinese and English news texts.