Activity-based linkage and ranking methods for personal dataspace

Abstract

This research was conducted to alleviate desktop activity support deficiency, which occurs due to the lack of links between desktop resources. The research exploited information such as associations, contexts, and activity information about accesses to local resources, and translated this information into a personal linkage structure. More specifically, the research presented a novel approach to link and rank desktop resources by analysing users' activities over time, and exploited the associative links of resources from their implicit access patterns. Furthermore, multiple ranking methods, such as frequency of access, recency of access, focus time, and connectivity of resources, were proposed. Prototype systems were also developed, and a user study was conducted to validate the effectiveness of the proposed methods. The results showed that ranking desktop resources by their relevance - as carried out in this research - could improve activity-specific support, as well as overall performance in the area of personal information management.