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Nevena Dragovic

dblp:168/1221 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
query formulation
0.212016
"Is Sven Seven?": A Search Intent Module for Children · SIGIR 2016
Information retrieval
query reformulation
0.212016
"Is Sven Seven?": A Search Intent Module for Children · SIGIR 2016
Information retrieval
query understanding
0.112016
"Is Sven Seven?": A Search Intent Module for Children · SIGIR 2016
Information retrieval › query understanding
search intent
0.112016
"Is Sven Seven?": A Search Intent Module for Children · SIGIR 2016
YearPublicationVenuePosition
2018 Looking for the Movie Seven or Sven from the Movie Frozen?: A Multi-perspective Strategy for Recommending Queries for Children
abstract
Popular search engines are usually tuned to satisfy the information needs of a general audience. As a result, non-traditional, yet active groups of users, such as children, experience challenges composing queries that can lead them to the retrieval of adequate results. To aid young users in formulating keyword queries that can facilitate their information-seeking process, we introduce ReQuIK, a multi-perspective query suggestion system for children. ReQuIK informs its suggestion process by applying (i) a strategy based on search intent to capture the purpose of a query, (ii) a ranking strategy based on a wide and deep neural network that considers both raw text and traits commonly associated with kid-related queries, (iii) a filtering strategy based on the readability levels of documents potentially retrieved by a query to favor suggestions that trigger the retrieval of documents matching children»s reading skills, and (iv) a content-similarity strategy to ensure diversity among suggestions. For assessing the quality of the system, we conducted initial offline and online experiments based on 591 queries written by 97 children, ages 6 to 13. The results of this assessment verified the correctness of ReQuIK»s recommendation strategy, the fact that it provides suggestions that appeal to children and ReQuIK»s ability to recommend queries that lead to the retrieval of materials with readability levels that correlate with children»s reading skills.
Ion Madrazo Azpiazu, Nevena Dragovic, Oghenemaro Anuyah, Maria Soledad Pera
CHIIR2
2017 Online searching and learning: YUM and other search tools for children and teachers
Ion Madrazo Azpiazu, Nevena Dragovic, Maria Soledad Pera, Jerry Alan Fails
Inf. Retr. J.2
2016 "Is Sven Seven?": A Search Intent Module for Children
abstract
The Internet is the biggest data-sharing platform, comprised of an immeasurable quantity of resources covering diverse topics appealing to users of all ages. Children shape tomorrow's society, so it is essential that this audience becomes agile with searching information. Although young users prefer well-known search engines, their lack of skill in formulating adequate queries and the fact that search tools were not designed explicitly with children in mind, can result in poor outcomes. The reasons for this include children's limited vocabulary, which makes it challenging to articulate information needs using short queries, or their tendency to create queries that are too long, which translates to few or irrelevant retrieved results. To enhance web search environments in response to children's behaviors and expectations, in this paper we discuss an initial effort to verify well-known issues, and identify yet to be explored ones, that affect children in formulating (natural language or keyword) queries. We also present a novel search intent module developed in response to these issues, which can seamlessly be integrated with existing search engines favored by children. The proposed module interprets a child's query and creates a shorter and more concise query to submit to a search engine, which can lead to a more successful search session. Initial experiments conducted using a sample of children queries validate the correctness of the proposed search intent module.
Nevena Dragovic, Ion Madrazo Azpiazu, Maria Soledad Pera
SIGIR1