SwiftLink: Serendipitous Navigation Strategy for Large-Scale Document Collections

The multiplication of large-scale document collections has created the need for robust and adaptive access strategies in many applicative areas. In this paper, we depart from the traditional document search paradigm to move onto the construction of a collection navigation strategy. We thus detail a model where user clicks are taken as expression of interest rather than positive search feedback. In terms of the interpretation of user interaction, our model is close to the recommendation paradigm. However, it exploits user feedback in a navigational procedure to perform an approximation of interactive metric learning procedures. Our model is directly compatible with distributed data indexing and is therefore inherently built to cope with scalability issues. The evaluation of such models is discussed and implemented over the context of an image collection. Experiments are presented where the user browses a collection of images with changing center of focus in the course of the browsing operations. Results show that our model is effectively able to recommend the user a relevant path through the collection and to adapt according to variable expressed interests.