Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations

In this paper, we present a hybrid recommendation framework based on the combination of graph embeddings and contextual word representations. Our approach is based on the intuition that each of the above mentioned representation models heterogeneous (and equally important) information, that is worth to be taken into account to generate a recommendation. Accordingly, we propose a strategy to combine both the features, which is based on the following steps: first, we separately generate graph embeddings and contextual word representations by exploiting state-of-the-art techniques. Next, these embeddings are used to feed a deep architecture that learns a hybrid representation based on the combination of the single groups of features. Finally, we exploit the resulting embedding to identify suitable recommendations. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and results show that the use of a hybrid representation leads to an improvement of the predictive accuracy. Moreover, our approach overcomes several competitive baselines, thus confirming the validity of this work.