Personalization of Search Results using Interactive Intent Modeling

Antti Kangasrääsiö; Dorota Głowacka; Samuel Kaski

Personalization of Search Results using Interactive Intent Modeling

Antti Kangasrääsiö, Dorota Głowacka, Samuel Kaski

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

226 Lataukset (Pure)

Abstrakti

In interactive intent modeling the user participates in constructing a model of her search intent using an interactive user interface. This allows the user to iteratively refine the model used for personalizing her search results, even in the presence of high initial uncertainty of the search intent. This approach for personalization has been shown to be beneficial in exploratory information retrieval. This extended abstract summarizes the key results from past studies and suggests possibilities for the generalization of this approach.

Alkuperäiskieli	Englanti
Otsikko	ICML 2016 Workshop on Computational Frameworks for Personalization
Sivumäärä	6
Tila	Julkaistu - 24 kesäkuuta 2016
OKM-julkaisutyyppi	Ei oikeutettu
Tapahtuma	ICML Workshop on Computational Frameworks for Personalization - New York, Yhdysvallat Kesto: 24 kesäkuuta 2016 → 24 kesäkuuta 2016 https://sites.google.com/site/icml2016ersonalization/

Workshop

Workshop	ICML Workshop on Computational Frameworks for Personalization
Maa	Yhdysvallat
Kaupunki	New York
Ajanjakso	24/06/2016 → 24/06/2016
www-osoite	https://sites.google.com/site/icml2016ersonalization/

Pääsy asiakirjaan

ICML2016_preprintSubmitted manuscript, 341 KBLisenssi: Määrittelemätön

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Siteeraa tätä

Kangasrääsiö, A., Głowacka, D., & Kaski, S. (2016). Personalization of Search Results using Interactive Intent Modeling. teoksessa ICML 2016 Workshop on Computational Frameworks for Personalization

@inproceedings{2fd5ab37b5d14bd6bc5188dbaefa314f,

title = "Personalization of Search Results using Interactive Intent Modeling",

abstract = "In interactive intent modeling the user participates in constructing a model of her search intent using an interactive user interface. This allows the user to iteratively refine the model used for personalizing her search results, even in the presence of high initial uncertainty of the search intent. This approach for personalization has been shown to be beneficial in exploratory information retrieval. This extended abstract summarizes the key results from past studies and suggests possibilities for the generalization of this approach.",

author = "Antti Kangasr{\"a}{\"a}si{\"o} and Dorota G{\l}owacka and Samuel Kaski",

year = "2016",

month = jun,

day = "24",

language = "English",

booktitle = "ICML 2016 Workshop on Computational Frameworks for Personalization",

note = "ICML Workshop on Computational Frameworks for Personalization ; Conference date: 24-06-2016 Through 24-06-2016",

url = "https://sites.google.com/site/icml2016ersonalization/",

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T1 - Personalization of Search Results using Interactive Intent Modeling

AU - Kangasrääsiö, Antti

AU - Głowacka, Dorota

AU - Kaski, Samuel

PY - 2016/6/24

Y1 - 2016/6/24

N2 - In interactive intent modeling the user participates in constructing a model of her search intent using an interactive user interface. This allows the user to iteratively refine the model used for personalizing her search results, even in the presence of high initial uncertainty of the search intent. This approach for personalization has been shown to be beneficial in exploratory information retrieval. This extended abstract summarizes the key results from past studies and suggests possibilities for the generalization of this approach.

AB - In interactive intent modeling the user participates in constructing a model of her search intent using an interactive user interface. This allows the user to iteratively refine the model used for personalizing her search results, even in the presence of high initial uncertainty of the search intent. This approach for personalization has been shown to be beneficial in exploratory information retrieval. This extended abstract summarizes the key results from past studies and suggests possibilities for the generalization of this approach.

M3 - Conference contribution

BT - ICML 2016 Workshop on Computational Frameworks for Personalization

T2 - ICML Workshop on Computational Frameworks for Personalization

Y2 - 24 June 2016 through 24 June 2016

ER -