一句话：提出用推荐系统为可适应软件仪表盘推荐指标，缓解固定指标集在多用户与快速变化需求下的失效问题。
仪表盘是一类以人为中心、旨在利用人类感知能力增强认知并支持有效决策的软件系统，在业务绩效管理、商业智能和内部控制中具有重要作用。然而，随着业务需求快速且持续变化，仪表盘若始终展示同一组指标，很快会难以有效传达关键信息，尤其当多个用户共同使用时。该论文因此关注可适应软件仪表盘中的指标建议问题，提出将推荐系统机制用于向用户推荐合适的度量指标，并借助“precooked”仪表盘——即为普通用户构建一个有用的默认模板——来提升仪表盘的适应性与信息相关性。摘要未给出具体算法、实现细节或实验结果，但明确了以推荐机制缓解固定指标集失效的研究方向。
Dashboards are software systems aiming to amplify cognition capitalizing on human perceptual abilities. As such, they have intrinsically a human-centric approach, meaning that their purpose is to support effective decision making. This has played a vital role in their success in business performance management, business intelligence, and internal control. However, as today's business requirements change rapidly and continuously, dashboards containing the same set of metrics throughout quickly become ineffective at conveying important information, especially when used by multiple users. This is one of the reasons for adopting the concept of "precooked" dashboards, i.e., building a default template that is useful to an average user.
DOI 原文 ·
@inproceedings{paperbot5770,
title = {Recommender systems: metric suggestion mechanisms applied to adaptable software dashboards},
author = {Dragos Strugar},
booktitle = {FSE},
year = {2020},
doi = {10.1145/3368089.3418779}
}