Abstract
Recommender systems aim at predicting the preference of a user towards a given item (e.g., a movie, a song). For systems that must cope with continuously evolving item catalogs, there will be a considerable rate of new items for which no past preference is known that could otherwise inform preference-based recommendations. In contrast, pure content-based recommendations may suffer from noisy item descriptions. To overcome these problems, we propose an information-theoretic approach that exploits a taxonomy of categories associated with the cataloged items in order to select informative terms for an improved recommendation. Our experiments using two publicly available datasets attest the effectiveness of the proposed approach, which significantly outperforms state-of-the-art content-based recommenders from the literature.
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
Preview
Unable to display preview. Download preview PDF.
References
Bambini, R., Cremonesi, P., Turrin, R.: A recommender system for an IPTV service provider: A real large-scale production environment. In: Recommender Systems Handbook, pp. 299–331. Springer (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys, pp. 39–46 (2010)
Cremonesi, P., Turrin, R., Airoldi, F.: Hybrid algorithms for recommending new items. In: HetRec, pp. 33–40 (2011)
Furnas, G.W., Deerwester, S., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. In: SIGIR, pp. 465–480 (1988)
Gedikli, F., Jannach, D.: Recommending based on rating frequencies. In: RecSys, pp. 233–236 (2010)
Gunawardana, A., Meek, C.: Tied boltzmann machines for cold start recommendations. In: RecSys, pp. 19–26 (2008)
Leung, C.W.-K., Chan, S.C.-F., Chung, F.-l.: An empirical study of a cross-level association rule mining approach to cold-start recommendations. Know.-Based Syst., 515–529 (2008)
Lops, P., Gemmis, M., Semeraro, G.: Content-based recommender systems: State of the art and trends. In: Recommender Systems Handbook. Springer (2011)
Park, S.-T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: RecSys, pp. 21–28 (2009)
Pilászy, I., Tikk, D.: Recommending new movies: even a few ratings are more valuable than metadata. In: RecSys, pp. 93–100 (2009)
Qumsiyeh, R., Ng, Y.-K.: Predicting the ratings of multimedia items for making personalized recommendations. In: SIGIR, pp. 475–484 (2012)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR, pp. 253–260 (2002)
Schifanella, R., Panisson, A., Gena, C., Ruffo, G.: Mobhinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networks. In: RecSys, pp. 27–34 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Costa, T.F., Lacerda, A., Santos, R.L.T., Ziviani, N. (2014). Information-Theoretic Term Selection for New Item Recommendation. In: Moura, E., Crochemore, M. (eds) String Processing and Information Retrieval. SPIRE 2014. Lecture Notes in Computer Science, vol 8799. Springer, Cham. https://doi.org/10.1007/978-3-319-11918-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-11918-2_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11917-5
Online ISBN: 978-3-319-11918-2
eBook Packages: Computer ScienceComputer Science (R0)