Bundle recommendation aims to provide users with convenient one-stop solutions by recommending bundles of related items that cater to their diverse needs. However, previous research has neglected the interaction betwe...Bundle recommendation aims to provide users with convenient one-stop solutions by recommending bundles of related items that cater to their diverse needs. However, previous research has neglected the interaction between bundle and item views and relied on simplistic methods for predicting user-bundle relationships. To address this limitation, we propose Hybrid Contrastive Learning for Bundle Recommendation (HCLBR). Our approach integrates unsupervised and supervised contrastive learning to enrich user and bundle representations, promoting diversity. By leveraging interconnected views of user-item and user-bundle nodes, HCLBR enhances representation learning for robust recommendations. Evaluation on four public datasets demonstrates the superior performance of HCLBR over state-of-the-art baselines. Our findings highlight the significance of leveraging contrastive learning and interconnected views in bundle recommendation, providing valuable insights for marketing strategies and recommendation system design.展开更多
Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform d...Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform data analysis stored in the repository, far away from the site, where users work, allowing them to generate knowledge regardless of their physical location. This paper presents new data analysis methods and new ways to detect people work location via mobile computing technology. The growing number of applications, content, and data can be accessed from a wide range of devices. It becomes necessary to introduce a centralized mobile device management. MDM is a KDE software package working with enterprise systems using mobile devices. The paper discussed the design system in detail.展开更多
文摘Bundle recommendation aims to provide users with convenient one-stop solutions by recommending bundles of related items that cater to their diverse needs. However, previous research has neglected the interaction between bundle and item views and relied on simplistic methods for predicting user-bundle relationships. To address this limitation, we propose Hybrid Contrastive Learning for Bundle Recommendation (HCLBR). Our approach integrates unsupervised and supervised contrastive learning to enrich user and bundle representations, promoting diversity. By leveraging interconnected views of user-item and user-bundle nodes, HCLBR enhances representation learning for robust recommendations. Evaluation on four public datasets demonstrates the superior performance of HCLBR over state-of-the-art baselines. Our findings highlight the significance of leveraging contrastive learning and interconnected views in bundle recommendation, providing valuable insights for marketing strategies and recommendation system design.
文摘Client software on mobile devices that can cause the remote control perform data mining tasks and show production results is significantly added the value for the nomadic users and organizations that need to perform data analysis stored in the repository, far away from the site, where users work, allowing them to generate knowledge regardless of their physical location. This paper presents new data analysis methods and new ways to detect people work location via mobile computing technology. The growing number of applications, content, and data can be accessed from a wide range of devices. It becomes necessary to introduce a centralized mobile device management. MDM is a KDE software package working with enterprise systems using mobile devices. The paper discussed the design system in detail.
