A Study of Model Predicting User Attributes Based on Semi-supervised Learning by Ladder Network

Abstract

In recent years, marketing using attribute information associated with member accounts of online services has been widely used. However, the majority of users are non-members who use services without registering for an account, and it is difficult to implement measures using attribute information for these non-member users. In order to deal with this situation, semi-supervised learning is an effective way to increase the number of users with attribute information by predicting it from the history data of member users who have attribute information, using the history data of non-member users as well. One of such semi-supervised learning methods is the Ladder Network, which is a neural network based model with adding and removing noise. This model provides highly accurate prediction for image data, and is also considered to be useful for predicting user attributes from historical data, where the feature vector is high-dimensional. However, this method cannot be applied to the case where the label takes ordered value, such as the user's age category. In this study, we propose an extended model based on the Ladder Network that incorporates a mechanism that can appropriately predict the user's attribute information. We also conduct an evaluation experiment using actual browsing history data to show the effectiveness of the proposed method.