Scalable multi-dimensional user intent identification using tree structured distributions

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Sökning: WFRF:(Benavides R) > (2010-2014) > Scalable multi-dime...

2 av 2
Föregående post
Nästa post
Till träfflistan

LIBRIS Formathandbok (Information om MARC21)

Fältnamn	Indikatorer	Metadata
000		03105naa a2200421 4500
001		oai:research.chalmers.se:ee966743-8fd9-4956-879e-889ab65df329
003		SwePub
008		171007s2011 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
020		a 9781450309349
024	7	a https://doi.org/10.1145/2009916.20099712 DOI
024	7	a https://research.chalmers.se/publication/2162942 URI
040		a (SwePub)cth
041		a engb eng
042		9 SwePub
072	7	a kon2 swepub-publicationtype
072	7	a ref2 swepub-contenttype
100	1	a Jethava, Vinay,d 1982u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)jethava
245	1 0	a Scalable multi-dimensional user intent identification using tree structured distributions
264		c 2011-07-24
264	1	a New York, NY, USA :b ACM,c 2011
520		a The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Datavetenskap0 (SwePub)102012 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Computer Sciences0 (SwePub)102012 hsv//eng
653		a Chow-liu
653		a FastQ
653		a WordNet
653		a Facets
653		a Web search
653		a Query intent
700	1	a Calderón-Benavides, L.4 aut
700	1	a Baeza-Yates, R.A.u Yahoo Research Barcelona4 aut
700	1	a Bhattacharyya, C.u Indian Institute of Science4 aut
700	1	a Dubhashi, Devdatt,d 1965u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)dubhashi
710	2	a Chalmers tekniska högskolab Yahoo Research Barcelona4 org
773	0	t SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrievald New York, NY, USA : ACMg , s. 395-404q <395-404z 9781450309349
856	4	u http://dx.doi.org/10.1145/2009916.2009971y FULLTEXT
856	4 8	u https://doi.org/10.1145/2009916.2009971
856	4 8	u https://research.chalmers.se/publication/216294

Om ämnet

NATURVETENSKAP: NATURVETENSKAP; och Data och informa ...; och Datavetenskap

Artiklar i publikationen: SIGIR'11 - Proce ...

Av lärosätet: Chalmers tekniska högskola

Sök utanför SwePub

Sök vidare i:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se

Hitta via bibliotek

Till lärosätets databas

Hitta mer i SwePub

Sök utanför SwePub