Considering Time and Feature Entropy in Calibrated Recommendations

The essence of calibration in recommender systems is to generate recommendations that match the distribution of a given user’s past preferences regarding certain item features—e.g., in terms of preferred genres in the case of movies—while preserving relevance. The user’s past preference distribution is usually derived by considering the features of all items that the user previously liked. However, the most common approach in the literature to derive this distribution has certain limitations. First, it does not consider that user preferences may change over time. Second, there are domains where the relevant item features are set-valued, e.g., a movie can have several genres. In such cases, existing calibration approaches may represent the true user’s preference distribution in a suboptimal way. In this work, we, therefore, propose two novel approaches to derive the preference distributions of users for the purpose of calibration. The first method allows us to decrease the relevance of possibly outdated preference information. The second method is an entropy-based approach, which aims to capture better the user’s true preferences toward certain item features. Extensive experimental evaluations on four distinct datasets confirm that the proposed techniques are more effective in reducing the level of miscalibration than the common state-of-the-art calibration approach.