.png){kind=icon}
返回
Generating Recommendations From Multiple Data Sources: A Methodological Framework for System Design and Its Application
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3028777.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recommender systems (RSs) are systems that produce individualized recommendations as output or drive the user in a personalized way to interesting or useful objects in a space of possible options. Recently, RSs emerged as an effective support for decision making. However, when people make decisions, they usually take into account different and often conflicting information such as preferences, long-term goals, context, and their current condition. This complexity is often ignored by RSs. In order to provide an effective decision-making support, a RS should be holistic i.e., it should rely on a complete representation of the user, encoding heterogeneous user features (such as personal interests, psychological traits, health data, social connections) that may come from multiple data sources. However, to obtain such holistic recommendations some steps are necessary: first, we need to identify the goal of the decision-making process; then, we have to exploit common-sense and domain knowledge to provide the user with the most suitable suggestions that best fit the recommendation scenario. In this article, we present a methodological framework that can drive researchers and developers during the design process of this kind of holistic RS. We also provide evidence of the framework validity by presenting the design process and the evaluation of a food RS based on holistic principles.
Keyword:
Recommender systems
Decision making
Mood
Knowledge based systems
Complexity theory
Cognition
Encoding
Theoretical framework
design methodology
recommender systems
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
UNCONSCIOUS CEREBRAL INITIATIVE AND THE ROLE OF CONSCIOUS WILL IN VOLUNTARY ACTION无意识的大脑主动性和意识意志在自愿行动中的作用
PREAI
分享收藏A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm内核上下文推荐系统 (KCR): 一种可扩展的上下文感知推荐系统算法IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions走向下一代推荐系统: 对最新技术和可能扩展的调查
PREAI
分享收藏Field-warp registration for biomedical high-resolution thermal infrared images场形变换配准在生物医学高分辨率热红外图像中的应用
PREAI
分享收藏
查看更多内容