Knowledge graphs have shown to be highly beneficial to recommender systems, providing an ideal data structure to generate hybrid recommendations using both content-based and collaborative filtering. Most knowledge-aware recommender systems are based on manually engineered features, typically relying on path counting and/or on random walks. Recently, knowledge graph embeddings have proven to be extremely effective at learning features for prediction tasks, reducing the complexity and time required to manually design effective features. In this work, we present entity2rec, which learns user-item relatedness for item recommendation through property-specific knowledge graph embeddings. A key element of entity2rec is the construction of property-specific subgraphs. Through an extensive evaluation on three datasets, we show that: (1) hybrid property-specific subgraphs consistently enhance the quality of recommendations with respect to collaborative and content-based subgraphs; (2) entity2rec generates accurate and non-obvious recommendations, compared to a set of state-of-the-art recommender systems, achieving high accuracy, serendipity and novelty. More in detail, entity2rec is particularly effective when the dataset is sparse and has a low popularity bias; (3) entity2rec is easily interpretable and can thus be configured for a particular recommendation problem.
entity2rec: Property-specific knowledge graph embeddings for item recommendation
Expert Systems with Applications, 22 January 2020
© Elsevier. Personal use of this material is permitted. The definitive version of this paper was published in Expert Systems with Applications, 22 January 2020 and is available at : https://doi.org/10.1016/j.eswa.2020.113235
PERMALINK : https://www.eurecom.fr/publication/6169