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Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization

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Abstract

Providing relevant personalized recommendations for new users is one of the major challenges in recommender systems. This problem, known as the user cold start has been approached from different perspectives. In particular, cross-domain recommendation methods exploit data from source domains to address the lack of user preferences in a target domain. Most of the cross-domain approaches proposed so far follow the paradigm of collaborative filtering, and avoid analyzing the contents of the items, which are usually highly heterogeneous in the cross-domain setting. Content-based filtering, however, has been successfully applied in domains where item content and metadata play a key role. Such domains are not limited to scenarios where items do have text contents (e.g., books, news articles, scientific papers, and web pages), and where text mining and information retrieval techniques are often used. Potential application domains include those where items have associated metadata, e.g., genres, directors and actors for movies, and music styles, composers and themes for songs. With the advent of the Semantic Web, and its reference implementation Linked Data, a plethora of structured, interlinked metadata is available on the Web. These metadata represent a potential source of information to be exploited by content-based and hybrid filtering approaches. Motivated by the use of Linked Data for recommendation purposes, in this paper we present and evaluate a number of matrix factorization models for cross-domain collaborative filtering that leverage metadata as a bridge between items liked by users in different domains. We show that in case the underlying knowledge graph connects items from different domains and then in situations that benefit from cross-domain information, our models can provide better recommendations to new users while keeping a good trade-off between recommendation accuracy and diversity.

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Notes

  1. Facebook online social networking, https://www.facebook.com.

  2. Netflix streaming media and video provider, https://www.netflix.com.

  3. Spotify digital music service, https://www.spotify.com.

  4. Barnes & Noble online bookseller, http://www.barnesandnoble.com.

  5. Amazon electronic commerce site, https://www.amazon.com.

  6. eBay consumer-to-consumer and business-to-consumer sales, http://www.ebay.com.

  7. Facebook social network, https://www.facebook.com.

  8. Twitter online news and social networking service, https://twitter.com.

  9. Wikipedia online encyclopedia, https://www.wikipedia.org.

  10. Available at http://ir.ii.uam.es/metadata.

  11. Namespace for rdfs, http://www.w3.org/2000/01/rdf-schema.

  12. Namespace for rdf, http://www.w3.org/1999/02/22-rdf-syntax-ns#.

  13. Namespace for dbo, http://dbpedia.org/ontology.

  14. The YAGO knowledge base, http://www.mpi-inf.mpg.de/yago-naga/yago.

  15. Namespace for yago, http://dbpedia.org/class/yago.

  16. Namespace for dbr, http://dbpedia.org/resource.

  17. https://github.com/sisinflab/lodreclib.

  18. Code available at https://github.com/nachoft/cross-metadata-mf.

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Acknowledgements

This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P). The authors thank the reviewers for their thoughtful comments and suggestions.

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Authors and Affiliations

  1. Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain

    Ignacio Fernández-Tobías & Iván Cantador

  2. Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, Bari, Italy

    Paolo Tomeo, Vito Walter Anelli & Tommaso Di Noia

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  1. Ignacio Fernández-Tobías
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Fernández-Tobías, I., Cantador, I., Tomeo, P. et al. Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Model User-Adap Inter 29, 443–486 (2019). https://doi.org/10.1007/s11257-018-9217-6

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