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  Personalized Entity Search by Sparse and Scrutable User Profiles

Haratinezhad Torbati, G., Yates, A., & Weikum, G. (2021). Personalized Entity Search by Sparse and Scrutable User Profiles. Retrieved from https://arxiv.org/abs/2109.04713.

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arXiv:2109.04713.pdf (Preprint), 9KB
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 Urheber:
Haratinezhad Torbati, Ghazaleh1, Autor           
Yates, Andrew1, Autor           
Weikum, Gerhard1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Schlagwörter: Computer Science, Information Retrieval, cs.IR
 Zusammenfassung: Prior work on personalizing web search results has focused on considering
query-and-click logs to capture users individual interests. For product search,
extensive user histories about purchases and ratings have been exploited.
However, for general entity search, such as for books on specific topics or
travel destinations with certain features, personalization is largely
underexplored. In this paper, we address personalization of book search, as an
exemplary case of entity search, by exploiting sparse user profiles obtained
through online questionnaires. We devise and compare a variety of re-ranking
methods based on language models or neural learning. Our experiments show that
even very sparse information about individuals can enhance the effectiveness of
the search results.

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Sprache(n): eng - English
 Datum: 2021-09-102021
 Publikationsstatus: Online veröffentlicht
 Seiten: 5 p.
 Ort, Verlag, Ausgabe: -
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 Identifikatoren: arXiv: 2109.04713
BibTex Citekey: Haratinezhad2109.04713
URI: https://arxiv.org/abs/2109.04713
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