Situation-aware Multi-sided Personalised Analytics in Spatial Crowdsourcing

该项目旨在创建下一代推荐系统，增强空间众包平台上的任务分配和路线推荐。它希望解决大空间众包数据的态势感知可靠推荐方面的关键挑战，这对改善用户的服务体验和决策至关重要。该项目的预期成果包括先进的数据模型、高效的算法和查询技术，以创建一个有效、高效、人群引导和态势感知的人群引导高级空间众包分析（CASCA）系统。将惠及众包媒体数据分析和大数据领域，为澳大利亚产业和用户带来经济效益和社会效益。

This project aims to create a next generation recommender system that enables enhanced task allocation and route recommendation on spatial crowdsourcing platforms. It expects to address key challenges in situation-aware reliable recommendation for big spatial crowdsourcing data, which is vital in improving users’ service experience and decision making. Expected outcomes of this project include advanced data models, efficient algorithms and query techniques to create a Crowd-guided Advanced Spatial Crowdsourcing Analytics (CASCA) system that is effective, efficient, crowd-guided, and situation-aware. It will benefit crowdsourced media data analysis and big data fields, bringing economic and social benefits to Australian industries and users.