Supporting the Discovery of Long-Tail Resources on the Web
A plethora of resources made available via retrieval systems in digital libraries remains untapped in the so called long tail of the Web. These long-tail websites get considerably less visits than major Web hubs. Zero-effort queries ease the discovery of long-tail resources by proactively retrieving and presenting information based on a user’s context. However, zero-effort queries over existing digital library structures are challenging, since the underlying retrieval system is only accessible via an API. The information need must be expressed by a query, instead of optimizing the ranking between context and resources in the retrieval system directly. We address three research questions that arise from replacing the user information seeking process by zero-effort queries. Our first question addresses the transformation of a user query to an automatic query, derived from the context. We present means to 1) identify the relevant context on different levels of granularity, 2) derive an information need from the context via keyword extraction and personalization and 3) express this information need in a query scheme that avoids over- or under-specified queries. We address the cold start problem with an approach to bootstrap user profiles from social media, even for passive users. With the second question, we address the presentation of resources in zero-effort query scenarios, presenting guidelines for presentation interfaces in the browser and a visualization of the triadic relationship between context, query and results. QueryCrumbs, a compact query history visualization supports recalling information found in the past and exploratory search by visualizing qualitative and quantitative query similarity. Our last question addresses the gap between (simple) keyword queries and the representation of resources by rich and complex meta-data. We investigate and extend feature representation learning techniques centered around the skip-gram model with negative sampling. Finally, we present an approach to learn representations from network and text jointly that can cope with the partial absence of one modality. Experimental results show close to human performance of our zero-effort query and user profile generation approach and visualizations to be helpful in terms of transparency, efficiency and support for exploratory search. These results indicate that the proposed zero-effort query approach indeed eases the discovery of long-tail resources and the accompanying visualizations further facilitate this process. The joint representation model provides a first step to bridge the gap between query and resource representation and we plan to follow and investigate this route further in the future.
Networked Digital Library of Theses and Dissertations