Adaptive Feature Sampling for Recommendation with Missing Content Feature Values

Most recommendation algorithms mainly make use of user history interactions in the model, while these methods often suffer from the cold-start problem (user/item has no history information). On the other sides, content features help on cold-start scenarios for modeling new users or items. So it is essential to utilize content features to enhance different recommendation models. To take full advantage of content features, feature interactions such as cross features are used by some models and outperform than using raw features. However, in real-world systems, many content features are incomplete, e.g., we may know the occupation and gender of a user, but the values of other features (location, interests, etc.) are missing. This missing-feature-value (MFV) problem is harmful to the model performance, especially for models that rely heavily on rich feature interactions. Unfortunately, this problem has not been well studied previously.