Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Relevance feedback-based optimization of search queries for Patents
Linköpings universitet, Institutionen för datavetenskap, Interaktiva och kognitiva system.
2019 (engelsk)Independent thesis Advanced level (degree of Master (Two Years)), 40 poäng / 60 hpOppgave
Abstract [en]

In this project, we design a search query optimization system based on the user’s relevance feedback by generating customized query strings for existing patent alerts. Firstly, the Rocchio algorithm is used to generate a search string by analyzing the characteristics of related patents and unrelated patents. Then the collaborative filtering recommendation algorithm is used to rank the query results, which considering the previous relevance feedback and patent features, instead of only considering the similarity between query and patents as the traditional method.

In order to further explore the performance of the optimization system, we design and conduct a series of evaluation experiments regarding TF-IDF as a baseline method. Experiments show that, with the use of generated search strings, the proportion of unrelated patents in search results is significantly reduced over time. In 4 months, the precision of the retrieved results is optimized from 53.5% to 72%. What’s more, the rank performance of the method we proposed is better than the baseline method. In terms of precision, top10 of recommendation algorithm is about 5 percentage points higher than the baseline method, and top20 is about 7.5% higher. It can be concluded that the approach we proposed can effectively optimize patent search results by learning relevance feedback.

sted, utgiver, år, opplag, sider
2019. , s. 65
Emneord [en]
Patent Search, Query Reformulation, Recommendation System, Matrix Decomposition, Text Processing
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-154173ISRN: LIU-IDA/LITH-EX-A--19/007--SEOAI: oai:DiVA.org:liu-154173DiVA, id: diva2:1284224
Fag / kurs
Computer Engineering
Presentation
2018-09-20, Charlie, D-building, Linköping University, Linköping, 09:25 (engelsk)
Veileder
Examiner
Tilgjengelig fra: 2019-01-31 Laget: 2019-01-31 Sist oppdatert: 2019-02-01bibliografisk kontrollert

Open Access i DiVA

fulltext(1090 kB)78 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1090 kBChecksum SHA-512
fcfa769ebdef573c8cf9ace3e507fe015ae1c82e3e0059edc51a4298e65fdecb02343734a9e5fe902bca1b96a288a587b715faa25db4b6962313462aac85abb6
Type fulltextMimetype application/pdf

Søk i DiVA

Av forfatter/redaktør
Cheng, Sijin
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 78 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

urn-nbn

Altmetric

urn-nbn
Totalt: 193 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf