mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Real-Estate Recommendation Engine Based on User Navigation Behavior
Mälardalen University, School of Innovation, Design and Engineering.
2011 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Content recommendation has become popular in web applications of various types. It is used in e-commerce web pages, social networks and search engines. Different approaches are utilized which rely mostly on traditional server-side web log mining, page semantic analysis. However there is more information that can be used for recommendation of higher quality: user navigation behavior.

In this thesis work we propose a novel approach for recommendation which is based on pointing devices actions, e.g. clicks, right-clicks, trails, text selections, etc., combined together with user defined weights of various characteristics of presented content. Actions are captured on client-side with recommended content being instantly displayed on a user screen. Particularly this work is targeted at real estate search engines where real estate properties are being recommended based on how users navigate search results. The given problem was seen as a classification problem where user navigation behavior data served as a feature set, and machine learning algorithm was selected to solve it. Results of initial training data capture and analysis (84% classification accuracy) and then further model validation (79% of positive responses) were successful and this concept proved to be promising.

Place, publisher, year, edition, pages
2011. , 74 p.
Keyword [en]
recommendation, user navigation behavior, search engine, SERP, machine learning, eye gaze coordination, pointing device, mouse, web page layout, client side scripting
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-13325OAI: oai:DiVA.org:mdh-13325DiVA: diva2:458298
Subject / course
Computer Science
Presentation
2011-11-17, Västerås, 13:35 (English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2014-09-17 Created: 2011-11-22 Last updated: 2014-09-17Bibliographically approved

Open Access in DiVA

No full text

By organisation
School of Innovation, Design and Engineering
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

Total: 28 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf