Open AccessDeriving a categorical vector space model for web page recommendations based on Wikipedia's content
Author(s) -
Chang PeiChia,
Quiroga Luz M.
Publication year - 2010
Publication title -
proceedings of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1550-8390
pISSN - 0044-7870
DOI - 10.1002/meet.14504701442
Subject(s) - computer science , web page , information retrieval , categorical variable , vector space model , world wide web , categorization , space (punctuation) , page view , static web page , web navigation , artificial intelligence , machine learning , operating system
This work models web pages and users for web page recommendation by deriving a categorical vector space model (WikiVSM). We augment Wikipedia's categorization system with keywords extracted from Wikipedia' pages. By applying keyword matching, any web page can be mapped into WikiVSM. We compare WikiVSM's performance with Vector Space Model (VSM) regarding recommending topically relevant web pages to individual users based on their browsing history. Results indicate that WikiVSM performs significantly better in generating recommendations. This is possibly due to features of Wikipedia and our augmentation.