Hierarchical Latent Context Representation for CARS

Abstract

In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-aware recommendation algorithms that extend collaborative filtering (CF) approaches and utilize structured and unstructured latent contextual information. In particular, the first algorithm utilizes structured latent contexts and the second one combines the structured and the unstructured latent contextual representations. By using latent contextual information in a recommendation model, we capture and represent both the structure of the latent context in the form of a hierarchy and the values of contextual variables in the form of an unstructured vector. We tested the two proposed methods with two CF-based methods on several context-rich datasets under different experimental settings. We show that using hierarchical latent contextual representations leads to significantly better recommendations than the baselines for the datasets having high- and medium-dimensional contexts. Although this is not the case for the low-dimensional contextual data, the hybrid approach, combining structured and unstructured latent contextual information, significantly outperforms other baselines across all the experimental settings and dimensions of contextual data.