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Taxonomy-Induced Matrix Factorization for Inferring Preference of Mobile Telecom Users

User preference profile is generally significant to marketing strategy decisions as well as user experience improvement for mobile telecom operators. To establish preference profile, perators create a hierarchical taxonomy of preference and classify records of user browsing history on mobile internet by the taxonomy to measure user preference. However, the incompleteness of recorded browsing history makes it nontrivial to observe all the users' preferences. To complete missing preferences, recommendation based methodology is commonly exploited. Although taxonomy contains the semantic relationships between preferences, there are merely a few works that explored them for recommendation. We extend these works by clearly defining the relation types and learning relation strengths among preferences in the taxonomy, on which we propose a Taxonomy-induced Matrix Factorization (TMF) model. We perform experiments on a large dataset of user browsing data from a Chinese telecom operator. The results show that our proposed model outperforms the standard matrix factorization model. In addition, the relations learned by TMF are detailed analyzed to show their inherent effects for the inference improvement.