Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder

Abstract

The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics. Among all the real-world application scenarios, feature extraction from knowledge graph (KG) for personalized recommendation has achieved substantial performance for addressing the problem of information overload. However, the rating matrix of recommendations is usually sparse, which may result in significant performance degradation. The crucial problem is how to extract and extend features from additional side information. To address these issues, we propose a novel feature representation learning method for the recommendation in this paper that extends item features with knowledge graph via triple-autoencoder. More specifically, the comment information between users and items is first encoded as sentiment classification. These features are then applied as the input to the autoencoder for generating the auxiliary information of items. Second, the item-based rating, the side information, and the generated comment representations are incorporated into the semi-autoencoder for reconstructed output. The low-dimensional representations of this extended information are learned with the semi-autoencoder. Finally, the reconstructed output generated by the semi-autoencoder is input into a third autoencoder. A serial connection between the semi-autoencoder and the autoencoder is designed here to learn more abstract and higher-level feature representations for personalized recommendation. Extensive experiments conducted on several real-world datasets validate the effectiveness of the proposed method compared to several state-of-the-art models.

Keywords: autoencoder; collaborative filtering; personalized recommendation; representation learning; semi-autoencoder.

FIGURE 1

Illustration of a semi-autoencoder where the input and output layers can be inconsistent. The length of the input layer is longer/shorter than the output layer in the left/right part.

FIGURE 2

Whole framework of the proposed KGTA

FIGURE 3

RMSE of our KGTA and compared methods on the MovieLens 100K dataset.

FIGURE 4

RMSE of our KGTA and compared methods on the MovieLens 1M dataset.

FIGURE 5

The parameter influence of the number of hidden layer neurons on our KGTA. (A) The influence performance on MovieLens 100K. (B) The influence performance on MovieLens 1M.

FIGURE 6

The parameter influence of the number of epochs on our KGTA. (A) The influence performance on MovieLens 100K. (B) The influence performance on MovieLens 1M.

FIGURE 7

The parameter influence of the length of comments on our KGTA. (A) The influence performance on MovieLens 100K. (B) The influence performance on MovieLens 1M.