Skip to main content

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

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.

This is a preview of subscription content, access via your institution.

Fig. 1

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.

References

  • Abel, F., Araújo, S., Gao, Q., Houben, G.J.: Analyzing cross-system user modeling on the social web. In: Proceedings of the 11th International Conference on Web Engineering (ICWE’11), pp. 28–43 (2011)

  • Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User Adap. Interact. 23(2–3), 169–209 (2013)

    Article  Google Scholar 

  • Bell, R.M., Koren, Y.: Lessons from the Netflix prize challenge. SIGKDD Explor. 9(2), 75–79 (2007a)

    Article  Google Scholar 

  • Bell, R.M., Koren, Y.: Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07), pp. 43–52 (2007b)

  • Bellogín, A., Fernández-Tobías, I., Cantador, I., Tomeo, P.: Neighbor selection for cold users in collaborative filtering with positive-only feedback. In: Proceedings of the 18th Conference of the Spanish Association for Artificial Intelligence (CAEPIA’18), pp. 3–12 (2018)

  • Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: Proceedings of the 1st ACM Conference on Recommender Systems (RecSys’07), pp. 33–40 (2007a)

  • Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Model. User Adapt. Interact. 18(3), 245–286 (2007b)

    Article  Google Scholar 

  • Bizer, C., Heath, T., Berners-Lee, T.: Linked data—the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)

    Article  Google Scholar 

  • Cantador, I., Fernández-Tobías, I., Bellogín, A.: Relating personality types with user preferences in multiple entertainment domains. In: Proceeding of the 1st International Workshop on Emotions and Personality in Personalized Services (EMPIRE’13) (2013)

  • Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 919–959. Springer, Berlin (2015)

    Chapter  Google Scholar 

  • Cao, B., Liu, N.N., Yang, Q.: Transfer learning for collective link prediction in multiple heterogenous domains. In: Proceedings of the 27th International Conference on Machine Learning (ICML’10), pp. 159–166 (2010)

  • Chung, R., Sundaram, D., Srinivasan, A.: Integrated personal recommender systems. In: Proceedings of the 9th International Conference on Electronic Commerce (ICE’07), pp. 65–74 (2007)

  • Cremonesi, P., Quadrana, M.: Cross-domain recommendations without overlapping data: myth or reality? In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14), pp. 297–300 (2014)

  • Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: Proceedings of the 11th IEEE International Conference on Data Mining Workshops (ICDMW’11), pp. 496–503 (2011)

  • Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  • Di Noia, T., Ostuni, V.C., Tomeo, P., Di Sciascio, E.: Sprank: semantic path-based ranking for top-n recommendations using Linked Open Data. ACM Trans. Intell. Syst. Technol. 8(1), 9 (2016)

    Article  Google Scholar 

  • Enrich, M., Braunhofer, M., Ricci, F.: Cold-start management with cross-domain collaborative filtering and tags. In: Proceedings of the 14th International Conference on E-Commerce and Web Technologies (EC-Web’13), pp. 101–112 (2013)

  • Fernández-Tobías, I., Cantador, I.: Exploiting social tags in matrix factorization models for cross-domain collaborative filtering. In: Proceedings of the 1st Workshop on New Trends in Content-based Recommender Systems (CBRecSys’14), pp. 34–41 (2014)

  • Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: A generic semantic-based framework for cross-domain recommendation. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec’11), pp. 25–32 (2011)

  • Fernández-Tobías, I., Cantador, I., Plaza, L.: An emotion dimensional model based on social tags: crossing folksonomies and enhancing recommendations. In: Proceedings of the 14th International Conference on E-Commerce and Web Technologies (EC-Web’13), pp. 88–100 (2013)

  • Funk, S.: Netflix update: try this at home. http://sifter.org/~simon/journal/20061211.html (2006). Accessed 12 Dec 2017

  • Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07), pp. 1606–1611 (2007)

  • Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Proceedings of 23th and 16th European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD’13), pp. 161–176 (2013)

  • Givon, S., Lavrenko, V.: Predicting social-tags for cold start book recommendations. In: Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09), pp. 333–336 (2009)

  • Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., Teixeira, R.: Exploiting innocuous activity for correlating users across sites. In: Proceedings of the 22nd International World Wide Web Conference (WWW’13), pp. 447–458 (2013)

  • Guo, G., Zhang, J., Sun, Z., Yorke-Smith, N.: Librec: A Java library for recommender systems. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of the 23rd Conference on User Modeling, Adaptation, and Personalization (UMAP’15) (2015)

  • He, M., Zhang, J., Yang, P., Yao, K.: Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining (WSDM’18), pp. 225–233 (2018)

  • Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), pp. 230–237 (1999)

  • Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Zhu, C.: Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22nd International World Wide Web Conference (WWW’13) pp. 595–605 (2013)

  • Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08), pp. 263–272 (2008)

  • Hulpus, I., Prangnawarat, N., Hayes, C.: Path-based semantic relatedness on linked data and its use to word and entity disambiguation. In: Proceedings of the 14th International Semantic Web Conference (ISWC’15), pp. 442–457 (2015)

  • Jain, P., Kumaraguru, P., Joshi, A.: @i seek ’fb.me’: identifying users across multiple online social networks. In: Proceedings of the 22nd International World Wide Web Conference (WWW’13), pp. 1259–1268 (2013)

  • Kabbur, S., Ning, X., Karypis, G.: FISM: Factored item similarity models for top-n recommender systems. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13), pp. 659–667 (2013)

  • Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I.: Ontology-based identification of music for places. In: Proceedings of the 13th International Conference on Information Communication Technologies in Tourism (ENTER’13), pp. 436–447 (2013)

  • Kluver, D., Konstan, J.A.: Evaluating recommender behavior for new users. In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14), pp. 121–128 (2014)

  • Koren, Y.: Factorization meets the neighborhood: A multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08), pp. 426–434 (2008)

  • Koren, Y., Bell, R.M.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Berlin (2015)

    Chapter  Google Scholar 

  • Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: DBpedia—a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web J. 6(2), 167–195 (2015)

    Google Scholar 

  • Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI’09), pp. 2052–2057 (2009a)

  • Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th Annual International Conference on Machine Learning (ICML’09), pp. 617–624 (2009b)

  • Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  • Liu, J., Shi, J., Cai, W., Liu, B., Pan, W., Yang, Q., Ming, Z.: Transfer learning from APP domain to news domain for dual cold-start recommendation. In: Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning (RecSysKTL’17), pp. 38–41 (2017)

  • Loni, B., Shi, Y., Larson, M., Hanjalic, A.: Cross-domain collaborative filtering with factorization machines. In: Proceedings of the 36th European Conference on IR Research (ECIR’14), pp. 656–661 (2014)

  • Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links. In: Proceedings of the 2008 AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy, pp. 25–30 (2008)

  • Mirbakhsh, N., Ling, C.X.: Improving top-n recommendation for cold-start users via cross-domain information. ACM Trans. Knowl. Discov. Data 9(4), 33:1–33:19 (2015)

    Article  Google Scholar 

  • Pan, W.: A survey of transfer learning for collaborative recommendation with auxiliary data. Neurocomputing 177(C), 447–453 (2016)

    Article  Google Scholar 

  • Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI’10), pp. 210–235 (2010)

  • Pan, W., Liu, N.N., Xiang, E.W., Yang, Q.: Transfer learning to predict missing ratings via heterogeneous user feedbacks. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI’11), pp. 2318–2323 (2011)

  • Paquet, U., Koenigstein, N.: One-class collaborative filtering with random graphs. In: Proceedings of the 22nd International World Wide Web Conference (WWW’03), pp. 999–1008 (2013)

  • Pilászy, I., Zibriczky, D., Tikk, D.: Fast ALS-based matrix factorization for explicit and implicit feedback datasets. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys’10), pp. 71–78 (2010)

  • Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI’09), pp. 452–461 (2009)

  • Rowe, M.: SemanticSVD++: Incorporating semantic taste evolution for predicting ratings. In: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (WI-IAT’14), pp. 213–220 (2014)

  • Sahebi, S., Brusilovsky, P.: Cross-domain collaborative recommendation in a cold-start context: the impact of user profile size on the quality of recommendation. In: Proceedings of the 21st International Conference on User Modeling, Adaptation, and Personalization (UMAP’13), pp. 289–295 (2013)

  • Sahebi, S., Brusilovsky, P., Bobrokov, V.: Cross-domain recommendation for large-scale data. In: Proceedings of the 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning (RecSysKTL’17), pp. 9–15 (2017)

  • Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems (NIPS’07), pp. 1257–1264 (2007)

  • Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system—a case study. In: Proceedings of the ACM WebKDD-2000 Workshop (2000)

  • Shadbolt, N., Berners-Lee, T., Hall, W.: The semantic web revisited. IEEE Intell. Syst. 21(3), 96–101 (2006)

    Article  Google Scholar 

  • Shapira, B., Rokach, L., Freilikhman, S.: Facebook single and cross domain data for recommendation systems. User Model. User Adapt. Interact. 23(2–3), 211–247 (2013)

    Article  Google Scholar 

  • Shi, Y., Larson, M., Hanjalic, A.: Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: Proceedings of the 19th International Conference on User Modeling, Adaption and Personalization (UMAP’11), pp. 305–316 (2011)

  • Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS’12), pp. 2960–2968 (2012)

  • Stewart, A., Diaz-Aviles, E., Nejdl, W., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Cross-tagging for personalized open social networking. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (Hypertext’09), pp. 271–278 (2009)

  • Szomszor, M., Alani, H., Cantador, I., O’Hara, K., Shadbolt, N.: Semantic modelling of user interests based on cross-folksonomy analysis. In: Proceedings of the 7th International Semantic Web Conference (ISWC’08), pp. 632–648 (2008a)

  • Szomszor, M., Cantador, I., Alani, H.: Correlating user profiles from multiple folksonomies. In: Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (Hypertext’08), pp. 33–42 (2008b)

  • Takács, G., Tikk, D.: Alternating least squares for personalized ranking. In: Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12), pp. 83–90 (2012)

  • Taneja, A., Arora, A.: Cross domain recommendation using multidimensional tensor factorization. Expert Syst. Appl. 92(C), 304–316 (2018)

    Article  Google Scholar 

  • Tiroshi, A., Kuflik, T.: Domain ranking for cross domain collaborative filtering. In: Proceedings of the 20th International Conference on User Modeling, Adaption and Personalization (UMAP’12), pp. 328–333 (2012)

  • Tiroshi, A., Berkovsky, S., Kâafar, M.A., Chen, T., Kuflik, T.: Cross social networks interests predictions based ongraph features. In: Proceedings of the 7th ACM Conference on Recommender Systems (RecSys’13), pp. 319–322 (2013)

  • Vargas, S., Baltrunas, L., Karatzoglou, A., Castells, P.: Coverage, redundancy and size-awareness in genre diversity for recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys’14), pp. 209–216 (2014)

  • Winoto, P., Tang, T.: If you like The Devil Wears Prada the book, will you also enjoy the Devil Wears Prada the movie? A study of cross-domain recommendations. New Gener. Comput. 26, 209–225 (2008)

    Article  Google Scholar 

  • Wongchokprasitti, C., Peltonen, J., Ruotsalo, T., Bandyopadhyay, P., Jacucci, G., Brusilovsky, P.: User model in a box: cross-system user model transfer for resolving cold start problems. In: Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (UMAP’15), pp. 289–301 (2015)

  • Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM’14), pp. 283–292 (2014)

  • Zhang, Y., Cao, B., Yeung, D.Y.: Multi-domain collaborative filtering. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI’10), pp. 725–732 (2010)

  • Zhang, Q., Wu, D., Lu, J., Liu, F., Zhang, G.: A cross-domain recommender system with consistent information transfer. Decis. Support Syst. 104, 49–63 (2017)

    Article  Google Scholar 

  • Zhao, L., Pan, S.J., Yang, Q.: A unified framework of active transfer learning for cross-system recommendation. Artif. Intell. 245, 38–55 (2017)

    MathSciNet  Article  MATH  Google Scholar 

  • Zhu, F., Wang, Y., Chen, C., Liu, G., Orgun, M., Wu, J.: A deep framework for cross-domain and cross-system recommendations. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18), pp. 3711–3717 (2018)

  • Zhuang, F., Luo, P., Xiong, H., Xiong, Y., He, Q., Shi, Z.: Cross-domain learning from multiple sources: a consensus regularization perspective. IEEE Trans. Knowl. Data Eng. 22(12), 1664–1678 (2010)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ignacio Fernández-Tobías.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11257-018-9217-6

Keywords

  • Cross-domain recommender systems
  • User cold start
  • Item metadata
  • Linked data