Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen" |
| Pages | 365-370 |
| Number of pages | 6 |
| Publication status | Published - 23 Sept 2019 |
| Externally published | Yes |
| Event | 2019 Conference on "Learning, Knowledge, Data, Analytics", LWDA 2019 - Berlin, Germany Duration: 30 Sept 2019 → 2 Oct 2019 |
Publication series
| Name | CEUR Workshop Proceedings |
|---|---|
| Publisher | CEUR WS |
| Volume | 2454 |
| ISSN (Print) | 1613-0073 |
Abstract
In scholarly search systems, computing recommendations of the same type, for example, additional publications when reading a particular publication, is a well-approached problem. However, suggesting items from another type, e.g., research data when reading a publication, is rarely covered in scholarly recommendations. In this position paper, we employ word embeddings to approach the problem of such cross-domain recommendations in scientific search systems, more specifically, recommending research data based on publications. Besides various metadata, publication and research dataset entries comprise textual metadata (e.g. title, abstract), which allows to detect similar entries using word embeddings. We illustrate first results, major problems and possible solutions when using word embeddings for recommending datasets based on publications.
Keywords
- Cross-domain recommendations, Dataset retrieval and recommendations, Word embeddings
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Cite this
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Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen". 2019. p. 365-370 (CEUR Workshop Proceedings; Vol. 2454).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Using Word Embeddings for Recommending Datasets based on Scientific Publications
AU - Tavakolpoursaleh, Narges
AU - Schaible, Johann
AU - Dietze, Stefan
PY - 2019/9/23
Y1 - 2019/9/23
N2 - In scholarly search systems, computing recommendations of the same type, for example, additional publications when reading a particular publication, is a well-approached problem. However, suggesting items from another type, e.g., research data when reading a publication, is rarely covered in scholarly recommendations. In this position paper, we employ word embeddings to approach the problem of such cross-domain recommendations in scientific search systems, more specifically, recommending research data based on publications. Besides various metadata, publication and research dataset entries comprise textual metadata (e.g. title, abstract), which allows to detect similar entries using word embeddings. We illustrate first results, major problems and possible solutions when using word embeddings for recommending datasets based on publications.
AB - In scholarly search systems, computing recommendations of the same type, for example, additional publications when reading a particular publication, is a well-approached problem. However, suggesting items from another type, e.g., research data when reading a publication, is rarely covered in scholarly recommendations. In this position paper, we employ word embeddings to approach the problem of such cross-domain recommendations in scientific search systems, more specifically, recommending research data based on publications. Besides various metadata, publication and research dataset entries comprise textual metadata (e.g. title, abstract), which allows to detect similar entries using word embeddings. We illustrate first results, major problems and possible solutions when using word embeddings for recommending datasets based on publications.
KW - Cross-domain recommendations
KW - Dataset retrieval and recommendations
KW - Word embeddings
UR - http://www.scopus.com/inward/record.url?scp=85073254235&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85073254235
T3 - CEUR Workshop Proceedings
SP - 365
EP - 370
BT - Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen"
T2 - 2019 Conference on "Learning, Knowledge, Data, Analytics", LWDA 2019
Y2 - 30 September 2019 through 2 October 2019
ER -