Serendipity in context: Prioritised contextual browsing in large‐scale digital libraries

Abstract

In previous work we proposed a model of information‐seeking behaviour in scholarly workset creation, combining and extending established models by Bates, Ellis, and Wilson to encompass strategies for scholarly research in large‐scale information systems. However, this model simplifies contextual browsing, a key aspect of information seeking in large‐scale information systems, as a single, holistic, strategy. Here, we extend this model with granular strategies for contextual browsing, defining new modes to characterise contextual browsing as combinations of these strategies, which we show to be consistent with serendipitous discovery as described by Makri et al. We study the properties of prioritised contextual browsing as a mechanism for implementing these strategies. We describe the Compage framework, a proof‐of‐concept implementation for prioritised contextual browsing of Linked Data resources, using Jaccard similarity for prioritisation. Extending Compage, we develop a simulation environment in which we investigate the utility of prioritised contextual browsing over a large‐scale digital library dataset. Our simulation applies three strategies for the traversal of contextual metadata: reset, unprioritised, and prioritised. Results empirically demonstrate the advantages of prioritised contextual browsing, and that elements of serendipity can be identified and incorporated within our information‐seeking model. In doing so, we evaluate our model's suitability for this scenario, yielding a more detailed understanding of the strategies and modes of behaviour underlying contextual browsing.

Serendipitous Learning Fostered by Brain State Assessment and Collective Wisdom

“…The message has been adopted by other researchers [12] that conclude their work with a strong support to "prioritization": In this paper we have extended our earlier model for information seeking in large-scale digital libraries with new strategies and modes of contextual browsing, showing their consistency with behaviors supporting serendipitous discovery set out by Makri et al We have described how contextual browsing requires prioritization to remain feasible in large-scale scenarios and have proposed the application of contextual similarity metrics to this purpose. However, the system "proof of concept" they used (Compage) did neither capitalize from the dynamic behavior of communities, nor from the assessment of brain mental states.…”

Combine or connect: Practical experiences querying library linked data

“…In this paper, we take a different approach by focusing instead on achieving crosswalk‐like mappings at query time through query normalization. We do this because our focus is on the creation of scholar‐generated worksets – machine‐actionable research collections supporting the requirements and information‐seeking behaviors of scholars undertaking research in large‐scale digital libraries (Weigl, Emsley, & Page, ; Weigl, Page, Organisciak, & Downie, ) – along with the metadata describing the objects contained within these collections. Inclusion of existing item‐level metadata is helpful to scholars refining worksets and assessing others' worksets for reuse.…”