Task-ontology Based Preference Estimation for Mobile Recommendation

Recommendations play an important role in Web-based commerce. Some advertisement agencies are now trying to push personalized recommendations to mobile phones. As mobile users almost always carry their mobile phones, it is important to recommend content that is related to the user’s real world activity in order to improve the quality of the recommendations. This paper realizes highly effective recommendations by proposing a method to estimate user preference based on the user’s real-world activity. This method has a couple of features. First, it uses a task-ontology for each preference segment to model the user’s real world activity(user action). The other feature is gathering words that allow user actions to be estimated from user history for each defined user action. We estimate the user’s preference and recommend content by incorporating the proposed method into a statistical SVM(Support Vector Machine) based recommendation algorithm. Finally, we conduct a user test and the result of this test shows 9% higher user evaluation scores of content recommendations compared to an existing content-based recommendation algorithm. This shows the effectiveness of the ontological approach in identifying words that allow user actions to be estimated when added to a statistical content-based recommendation algorithm.