Collaborative Filtering with Semantic Neighbour Discovery

  • Bruno Veloso
  • Benedita MalheiroEmail author
  • Juan C. Burguillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10022)


Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items – the subset of items co-rated by both users – typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process – a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.


User-based collaborative filtering Semantic neighbour discovery Semantic enrichment 



This work was partially financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI-01-0145-FEDER-006961»and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.


  1. 1.
    Bellogín, A., Castells, P., Cantador, I.: Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach. ACM Trans. Web (TWEB) 8(2), 12 (2014)Google Scholar
  2. 2.
    Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 28–37 (2001)CrossRefGoogle Scholar
  3. 3.
    Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)CrossRefGoogle Scholar
  4. 4.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM (1999)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)CrossRefGoogle Scholar
  6. 6.
    Kaveh-Yazdy, F., Zare-Mirakabad, M.R., Xia, F.: A novel neighbor selection approach for knn: a physiological status prediction case study. In: Proceedings of the 1st International Workshop on Context Discovery and Data Mining, p. 2. ACM (2012)Google Scholar
  7. 7.
    Kushwaha, N., Vyas, O.: Semmovierec: extraction of semantic features of dbpedia for recommender system. In: Proceedings of the 7th ACM India Computing Conference, p. 13. ACM (2014)Google Scholar
  8. 8.
    Martín-Vicente, M.I., Gil-Solla, A., Ramos-Cabrer, M., Blanco-Fernández, Y., López-Nores, M.: A semantic approach to avoiding fake neighborhoods in collaborative recommendation of coupons through digital tv. IEEE Trans. Consum. Electron. 56(1), 54–62 (2010)CrossRefGoogle Scholar
  9. 9.
    Martín-Vicente, M.I., Gil-Solla, A., Ramos-Cabrer, M., Pazos-Arias, J.J., Blanco-Fernández, Y., López-Nores, M.: A semantic approach to improve neighborhood formation in collaborative recommender systems. Expert Syst. Appl. 41(17), 7776–7788 (2014)CrossRefGoogle Scholar
  10. 10.
    Melville, P., Sindhwani, V.: Recommender systems. In: Encyclopedia of Machine Learning, pp. 829–838. Springer (2010)Google Scholar
  11. 11.
    Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 85–92. RecSys 2013, NY, USA. ACM, New York (2013)Google Scholar
  12. 12.
    Papagelis, M., Plexousakis, D.: Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Eng. Appl. Artif. Intell. 18(7), 781–789 (2005)CrossRefGoogle Scholar
  13. 13.
    Rey-López, M., Díaz-Redondo, R.P., Fernández-Vilas, A., Pazos-Arias, J.J.: T-learning 2.0: A personalised hybrid approach based on ontologies and folksonomies. In: Computational Intelligence for Technology Enhanced Learning, pp. 125–142. Springer (2010)Google Scholar
  14. 14.
    Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In: Proceedings of the Fifth International Conference on Computer and Information Technology, vol. 1 (2002)Google Scholar
  15. 15.
    Symeonidis, P., Nanopoulos, A., Papadopoulos, A.N., Manolopoulos, Y.: Collaborative filtering: Fallacies and insights in measuring similarity. In: Berendt, B., Hotho, A., Mladenic, D., Semeraro, G. (Chairs) Proceedings of the 17th European Conference on Machine Learning and 10th European Conference on Principles and the Practice of Knowledge Discovery in Databases Workshop on Web Mining, pp. 56–67 (2006)Google Scholar
  16. 16.
    Veloso, B., Malheiro, B., Burguillo, J.C.: A multi-agent brokerage platform for media content recommendation. Int. J. Appl. Math. Comput. Sci 25(3) (2015)Google Scholar
  17. 17.
    Vozalis, M.G., Margaritis, K.G.: Applying SVD on item-based filtering. In: Proceedings of the Fifth International Conference on Intelligent Systems Design and Applications, pp. 464–469. IEEE (2005)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bruno Veloso
    • 1
    • 2
  • Benedita Malheiro
    • 2
    • 3
    Email author
  • Juan C. Burguillo
    • 1
  1. 1.EET/UVigo – School of Telecommunication EngineeringUniversity of VigoVigoSpain
  2. 2.INESC TECPortoPortugal
  3. 3.ISEP/IPP – School of EngineeringPolytechnic Institute of PortoPortoPortugal

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