Improving One-class Collaborative Filtering with Manifold Regularization by Data-driven Feature Transformation

Manifold Regularization is introduced to the field of recommender system for combining additional features into collaborative filtering. However, most works choose features to incorporate intuitively or by domain knowledge. Besides, the effect of feature combination needs to be considered. Although some methods of dimension reduction help to transform raw features to latent space with combination for usage, these approaches cannot give an appropriate representation for the problem of recommendation because they are based only on the semantic of features. In this work, we design a data-driven framework for training of transformation function. For the idea of manifold regularization that similar features bring similar behavior, our approach uses an item-based method and a ranking framework to optimize the representation function of item''s feature from user''s feedback data. In the experiment, we show that for manifold regularized model, the transformed features through the proposed method can boost performance better for recommendation problem compared with raw feature and semantic-based transformed features. Besides, we also show our framework''s capability of detecting quality of feature for recommendation task.