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Web User Profiling in Online Advertising

Domen Košir (2015) Web User Profiling in Online Advertising. PhD thesis.

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    Online advertising is a multi-billion dollar industry. Big internet companies are therefore highly motivated to improve their user profiling methods and recommendation systems. We present a novel ontological profiling method AverageActionFC. It is based on time-based forgetting and profile correction with prototypes. The prototypes are a representation of domain knowledge and can be efficiently used to improve the quality of a user's profile. The experiments show that our method significantly outperforms existing methods. Collaborative filtering recommendation systems suffer from the cold start problem. We employ machine learning algorithms to increase the quality of recommendations for new users by predicting the latent factor values based on the semantic information in their profiles. We further improve the quality of recommendation lists by combining recommendations from two or more systems

    Item Type: Thesis (PhD thesis)
    Keywords: online advertising, profiling, recommendation systems, user tracking, privacy
    Number of Pages: 127
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    prof. dr. Igor Kononenko237Mentor
    izr. prof. dr. Zoran Bosnić3826Comentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536209091)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 2927
    Date Deposited: 17 Feb 2015 15:19
    Last Modified: 03 Mar 2015 09:51
    URI: http://eprints.fri.uni-lj.si/id/eprint/2927

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