Abstract
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.
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Notes
- 1.
Neo4j. https://neo4j.com.
- 2.
RecSys2013, https://www.kaggle.com/c/yelp-recsys-2013/data.
- 3.
Yelp. https://www.yelp.ie.
- 4.
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Acknowledgement
This paper is a result of research conducted within the “MSc in Artificial Intelligence and Data Analytics” of the Department of Applied Informatics of University of Macedonia. The presentation of the paper is funded by the University of Macedonia Research Committee.
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Kotzaitsis, D., Koloniari, G. (2024). A Multi-model Recurrent Knowledge Graph Embedding for Contextual Recommendations. In: Stefanidis, K., Systä, K., Matera, M., Heil, S., Kondylakis, H., Quintarelli, E. (eds) Web Engineering. ICWE 2024. Lecture Notes in Computer Science, vol 14629. Springer, Cham. https://doi.org/10.1007/978-3-031-62362-2_7
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