Supporting the Discovery of Long-Tail Resources on the Web
type: CreativeWork
description: 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. Final…
abstract: 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. Final…
author: Schlötterer, Jörg
date published: 2020-10-07
date modified: 2026-07-24T03:45:06Z
provider: Universität Passau
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Universität Passau