CAML: A Context-Aware Metric Learning approach for improved recommender systems

The primary goal of recommender systems is to identify and propose items that users might find appealing. A large number of these systems are heavily dependent on explicit interactions between the user and the item, which can often be infrequent. In this work, we introduce a unique model known as Context-Aware Metric Learning (CAML), designed to enhance the effectiveness of recommendations. The CAML model utilizes an attentive autoencoder to extract latent features from contextual context and incorporates these features into a metric learning framework. In particular, these extracted features act as a Gaussian prior for the embeddings of the items, thereby enhancing the precision of their positioning in the latent space. This integration not only boosts the precision of the recommendations but also increases computational efficiency, rendering CAML appropriate for both offline and online application scenarios. Our model's evaluation on two real-world datasets reveals that it outperforms several existing baseline models, including those that do not incorporate contextual information such as CML and CPE, as well as other contextual recommendation models like CDL, CATA, and CML+F.