Relation-aware collaborative autoencoder for personalized multiple facet selection

Abstract

Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users’ interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user–facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user–facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach.