Understanding and predicting cross-cultural food preferences with online recipe images     

《Information processing & management》2023,60(5):103443

Predicting food preferences is challenging due to the numerous factors that can influence individual taste. Cultural influences are one such factor that can significantly impact food preferences. Irrespective of culture, however, food visual aesthetics drive food choice. With this in mind, we study 15,000 images of food from prominent recipe portals in China, the US and Germany with the aim of identifying common visual trends related to food choice. We report that distinguishing between appreciated and less appreciated recipes based on visual features through multiple machine learning experiments is possible, with a maximum accuracy of 67%. The classifiers trained on one culture, when applied to other cultures, reveal that appreciated recipes from US and German portals share visual similarities, while the visual aspects making recipes attractive for Chinese users differ. Complementing our machine learning experiments, we conducted a user study with 450 participants from the three cultures. Participants are asked to rank recipes based on their appeal and provide justification through answering 19 questions. The results reveal significant predictors of preference, including perceived appearance and taste across all countries, and negative perception of perceived healthiness for US and German participants. Analysis of 77 consistently labelled appealing/non-appealing images reveal significant variations in low-level visual features such as colourfulness, sharpness, and contrast. These findings suggest that these low-level visual features may play a critical role in determining whether an image is perceived as appealing or not across different cultures. Our results offer promising insights into the development of cross-culture food recommender systems. By demonstrating common ground across food cultures, we believe that these systems can provide personalised meal suggestions that reflect user’s preferences while incorporating ideals such as healthfulness and sustainability.  相似文献   

公交流转系统研究     

宁冬梅 《科技广场》2010,(3):155-157

公文流转系统是目前比较流行的OA办公系统中的核心部分,本文主要从其发展趋势、主要功能模块、系统特点几个方面进行阐述。  相似文献   

网络文献传递服务:面向最终用户的资源共享   总被引：15，自引：0，他引：15  

许桂菊 《大学图书馆学报》2006,24(5):16-19

论述了网络文献传递服务正在变革着图书馆服务和资源共享的行为方式、生存与发展,并且引发资源共享和文献资源建设在理念和实践上的变化。对建设我国的网络文献传递服务系统提出了几点建议。  相似文献   

基于控制词集的中文信息动态自动聚类研究   总被引：1，自引：0，他引：1  

马张华  陈文广  金海燕  朱剑俊  崔会娟  刘贞君 《大学图书馆学报》2006,24(6):54-60

以专用词典为切分工具,建立以概念为基础的、具有主题分类特点的类目结构是中文信息动态自动聚类的一种适用方式。该文探索了基于控制词集的中文信息动态自动聚类技术,包括专用控制词集的构建,动态有限环境下的自动聚类程序、聚类算法,以及结合控制词集对聚类结果进行优化控制等,最后对实验结果给出了概要评价。  相似文献   

Self-Supervised learning for Conversational Recommendation     

《Information processing & management》2022,59(6):103067

  相似文献   

Fair and Explainable Depression Detection in Social Media     

《Information processing & management》2023,60(1):103168

Detection at an early stage is vital for the diagnosis of the majority of critical illnesses and is the same for identifying people suffering from depression. Nowadays, a number of researches have been done successfully to identify depressed persons based on their social media postings. However, an unexpected bias has been observed in these studies, which can be due to various factors like unequal data distribution. In this paper, the imbalance found in terms of participation in the various age groups and demographics is normalized using the one-shot decision approach. Further, we present an ensemble model combining SVM and KNN with the intrinsic explainability in conjunction with noisy label correction approaches, offering an innovative solution to the problem of distinguishing between depression symptoms and suicidal ideas. We achieved a final classification accuracy of 98.05%, with the proposed ensemble model ensuring that the data classification is not biased in any manner.  相似文献