A Multi-model Recurrent Knowledge Graph Embedding for Contextual Recommendations

Recommenders can be improved by exploiting the huge disposal of multi-context data that is now available. Knowledge Graphs (KGs) offer an intuitive way to incorporate this kind of assorted data. This paper introduces a context-aware recommender, based on deriving graph embeddings by learning the representations of appropriate meta-paths mined from a graph database. Our system uses several LSTMs to model the meta-path semantics between a user-item pair, based on the length of the mined path, a Multi-head Attention module as an attention mechanism, along with a pooling and a recommendation layer. Our evaluation shows that our system is on par with state-of-the-art recommenders, while also supporting contextual modeling.