AI

Not gone, but forgotten: Helping users re-find web pages by identifying those which are most likely to be lost

Abstract

We describe LostRank, a project in its formative stage which aims to produce a way to rank results in re-finding search engines according to the likelihood of their being lost to the user. To this end, we have explored a number of ideas, including applying users' temporal document access patterns to determine the documents that are both important and have not been recently accessed (indicating greater potential for loss), understanding users' topical access patterns to determine the topics that are more unfamiliar and hence more difficult to re-find documents within, and assessing users' difficulties in originally finding documents in order to predict future difficulties in re-finding them. As a position paper, we use this as an opportunity to describe early work, invite collaboration with others, and further the case for the use of temporal access patterns as a source for assisting users' re-finding of personal documents.