Abstract
Content-based recommender systems are widely used in different domains. However, they are usually inefficient to produce serendipitous recommendations. A recommendation is serendipitous if it is both relevant and unexpected. The literature indicates that one possibility of achieving serendipity in recommendations is to design them using partial similarities between items. From such intuition, coclustering can be explored to offer serendipitous recommendations to users. In this paper, we propose a coclustering-based approach to implement content-based recommendations. Experiments carried out on the MovieLens 2K dataset show that our approach is competitive in terms of serendipity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Since \(\#\tau _i\) is independent of the rating qualification, a “bad” item can still be popular.
- 2.
Since similarities among movies and tags have already been treated in the coclustering process, the maximization of J would insert an overspecialization in the process. The threshold score (0.25) was obtained empirically from extensive tests.
- 3.
The original Movielens dataset is provided by GroupLens research group (http://www.grouplens.org). In this study, the following files were used: movies.dat, movie_tags.dat, tags.dat and user_ratedmovies-timestamps.dat.
References
Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Billsus, D., Pazzani, M.J.: User modeling for adaptive news access. User Model. User-Adapt. Interact. 10(2–3), 147–180 (2000)
Cantador, I., Brusilovsky, P.L., Kuflik, T.: 2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec2011). ACM (2011)
Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 257–260. ACM (2010)
de Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. Inf. Process. Manag. 51(5), 695–717 (2015)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Iaquinta, L., De Gemmis, M., Lops, P., Semeraro, G., Filannino, M., Molino, P.: Introducing serendipity in a content-based recommender system. In: 8th International Conference on Hybrid Intelligent Systems (HIS 2008), pp. 168–173. IEEE (2008)
Iaquinta, L., De Gemmis, M., Lops, P., Semeraro, G., Molino, P.: Serendipitous encounters along dynamically personalized museum tours. In: Proceedings of the 1st Italian Information Retrieval Workshop (IIR 2010), pp. 101–102 (2010)
Ienco, D., Robardet, C., Pensa, R.G., Meo, R.: Parameter-less co-clustering for star-structured heterogeneous data. Data Min. Knowl. Discov. 26(2), 217–254 (2013)
Jenders, M., Lindhauer, T., Kasneci, G., Krestel, R., Naumann, F.: A serendipity model for news recommendation. In: Hölldobler, S., Krötzsch, M., Peñaloza, R., Rudolph, S. (eds.) KI 2015. LNCS (LNAI), vol. 9324, pp. 111–123. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24489-1_9
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Kotkov, D., Wang, S., Veijalainen, J.: A survey of serendipity in recommender systems. Knowl.-Based Syst. 111, 180–192 (2016)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3
Murakami, T., Mori, K., Orihara, R.: Metrics for evaluating the serendipity of recommendation lists. In: Satoh, K., Inokuchi, A., Nagao, K., Kawamura, T. (eds.) JSAI 2007. LNCS (LNAI), vol. 4914, pp. 40–46. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78197-4_5
Pensa, R.G., Boulicaut, J.F., Cordero, F., Atzori, M.: Co-clustering numerical data under user-defined constraints. Stat. Anal. Data Min. 3(1), 38–55 (2010)
Piao, S., Whittle, J.: A feasibility study on extracting twitter users’ interests using NLP tools for serendipitous connections. In: 3rd International Conference on Privacy, Security, Risk and Trust and 3rd International Conference on Social Computing, pp. 910–915. IEEE (2011)
Yoo, J., Choi, S.: Orthogonal nonnegative matrix tri-factorization for co-clustering: multiplicative updates on Stiefel manifolds. Inf. Process. Manag. 46(5), 559–570 (2010)
Zheng, Q., Ip, H.H.: Customizable surprising recommendation based on the tradeoff between genre difference and genre similarity. In: International Conference on WEB Intelligence and Intelligent Agent Technology, vol. 1, pp. 702–709. IEEE (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Silva, A.M., da Silva Costa, F.H., Diaz, A.K.R., Peres, S.M. (2018). Exploring Coclustering for Serendipity Improvement in Content-Based Recommendation. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-03493-1_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03492-4
Online ISBN: 978-3-030-03493-1
eBook Packages: Computer ScienceComputer Science (R0)