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