Relevance, diversity and serendipity in content recommendation using clustering

  • Fernando Henrique da Silva Costa USP
  • Andrei Martins Silva USP
  • Sarajane Marques Peres USP


In this paper, over-specialization in content-based recommender sys- tems is explored through the definition and analysis of recommendation strate- gies aiming at quality in terms of relevance, diversity and serendipity. Clustering is applied as the basis for building these strategies, applied to the news context. The results show the feasibility of the proposed strategies.


Adomavicius, G. and Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Eng., 17(6):734–749.

Arthur, D. and Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. In Proc. of the 18th annual ACM-SIAM symp. on discrete algorithms, pages 1027–1035. Soc. for Ind. and Applied Math.

Bobadilla, J., Ortega, F., Hernando, A., and Gutiérrez, A. (2013). Recommender systems survey. Knowl.-based Sys., 46:109–132.

Ge, M., Delgado-Battenfeld, C., and Jannach, D. (2010). Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proc. of the 4th ACM Conf. on Recommender Sys., pages 257–260. ACM.

Gong, S. (2010). A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. of Software, 5(7):745–752.

Hartigan, J. A. and Wong, M. A. (1979). Algorithm as 136: a k-means clustering algorithm. J. of the Royal Statistical Soc. - Series C (Applied Statistics), 28(1):100–108.

Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Trans. on Information Sys., 22(1):5–53.

Iaquinta, L., De Gemmis, M., Lops, P., Semeraro, G., Filannino, M., and Molino, P. (2008). Introducing serendipity in a content-based recommender system. In 8th Int. Conf. on Hybrid Intell. Sys., pages 168–173.

Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., and Naumann, F. (2015). A serendipity model for news recommendation. In Joint German/Austrian Conf. on Artificial Intell., pages 111–123. Springer.

Kotkov, D.,Wang, S., and Veijalainen, J. (2016). A survey of serendipity in recommender systems. Knowl.-based Sys., 111:180–192.

Liao, C.-L. and Lee, S.-J. (2016). A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electronic Commerce Research and Applications, 18:1–9.

Lops, P., De Gemmis, M., and Semeraro, G. (2011). Content-based recommender systems: state of the art and trends. In Recommender Sys. Handbook, pages 73–105. Springer.

Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. (2015). Recommender system application developments: a survey. Decision Support Sys., 74:12–32.

Massa, P. and Avesani, P. (2007). Trust-aware recommender systems. In Proc. of the 2007 ACM Conf. on Recommender Sys., pages 17–24. ACM.

Piao, S. and Whittle, J. (2011). A feasibility study on extracting twitter users’ interests using nlp tools for serendipitous connections. In IEEE 3rd Int. Conf. on Social Comp. and IEEE 3rd Int. Conf. on Privacy, Security, Risk and Trust, pages 910–915. IEEE.

Ricci, F., Rokach, L., and Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook, pages 1–35. Springer.

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. of Comp. and Applied Math., 20:53–65.

Salton, G., Wong, A., and Yang, C.-S. (1975). A vector space model for automatic indexing. Comm. of the ACM, 18(11):613–620.

Shani, G. and Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook, pages 257–297. Springer.

Silva, L. A., Peres, S. M., and Boscarioli, C. (2016). Introdução à mineração de dados: com Aplicações em R. Elsevier.

Strehl, A. and Ghosh, J. (2002). Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J. of Machine Learning Research, 3(Dec):583–617.

Xiao, Z., Che, F., Miao, E., and Lu, M. (2014). Increasing serendipity of recommender system with ranking topic model. Applied Math. & Information Sciences, 8(4):2041.

Zheng, Q. and Ip, H. H. (2012). Customizable surprising recommendation based on the tradeoff between genre difference and genre similarity. In IEEE/WIC/ACM Int. Conf. on WEB Intell. and Intell. Agent Technology, volume 1, pages 702–709. IEEE.

Zhong, S. (2005). Efficient online spherical k-means clustering. In Proc. Int. Joint Conf. on Neural Networks, volume 5, pages 3180–3185. IEEE.

Como Citar

Selecione um Formato
COSTA, Fernando Henrique da Silva; SILVA, Andrei Martins; PERES, Sarajane Marques. Relevance, diversity and serendipity in content recommendation using clustering. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 740-751. ISSN 2763-9061. DOI: