uRank: Exploring Document Recommendations through an Interactive User-Driven Approach.

Cecilia di Sciascio, Vedran Sabol, Eduardo E Veas

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.
Original languageEnglish
Title of host publicationIntRS@ RecSys
Pages29-36
Number of pages8
Publication statusPublished - 2015

Fingerprint

Recommender systems
Refining
Experiments

Cite this

uRank: Exploring Document Recommendations through an Interactive User-Driven Approach. / di Sciascio, Cecilia; Sabol, Vedran; Veas, Eduardo E.

IntRS@ RecSys. 2015. p. 29-36.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

@inproceedings{e6cfe139cfc14b5b9534b034d295a009,
title = "uRank: Exploring Document Recommendations through an Interactive User-Driven Approach.",
abstract = "Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.",
author = "{di Sciascio}, Cecilia and Vedran Sabol and Veas, {Eduardo E}",
year = "2015",
language = "English",
pages = "29--36",
booktitle = "IntRS@ RecSys",

}

TY - GEN

T1 - uRank: Exploring Document Recommendations through an Interactive User-Driven Approach.

AU - di Sciascio, Cecilia

AU - Sabol, Vedran

AU - Veas, Eduardo E

PY - 2015

Y1 - 2015

N2 - Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.

AB - Whenever we gather or organize knowledge, the task of searching inevitably takes precedence. As exploration unfolds, it becomes cumbersome to reorganize resources along new interests, as any new search brings new results. Despite huge advances in retrieval and recommender systems from the algorithmic point of view, many real-world interfaces have remained largely unchanged: results appear in an infinite list ordered by relevance with respect to the current query. We introduce uRank, a user-driven visual tool for exploration and discovery of textual document recommendations. It includes a view summarizing the content of the recommendation set, combined with interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. We provide a formal experiment showing that uRank users can browse the document collection and efficiently gather items relevant to particular topics of interest with significantly lower cognitive load compared to traditional list-based representations.

M3 - Conference contribution

SP - 29

EP - 36

BT - IntRS@ RecSys

ER -