Abstract
Many recommender systems lack in accuracy when the data used throughout the recommendation process is sparse. Our study addresses this limitation by means of a content boosted collaborative filtering approach applied to the task of movie recommendation. We combine two different approaches previously proved to be successful individually and improve over them by processing the content information of movies, as confirmed by our empirical evaluation results.
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
Heng Luo, Changyong Niu, Ruimin Shen, Carsten Ullrich, “A collaborative filtering framework based on both local user similarity and global user similarity,” in Proc. of ECML/PKDD 2008.
Ma, H., King, I., and Lyu, M. R., “Effective missing data prediction for collaborative filtering,” in Proc. of SIGIR 2007.
Souvik Debnath, Niloy Ganguly, Pabitra Mitra, “Feature weighting in content based recommendation system using social network analysis,” WWW, 2008
The Internet Movie Database (IMDb), http://www.imdb.com
IMDbPY, http://imdbpy.sourceforge.net/
MovieLens, www.movielens.umn.edu
Floyd, Robert W. (June 1962). "Algorithm 97: Shortest Path". Communications of the ACM 5 (6): 345.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this paper
Cite this paper
Özbal, G., Karaman, H., Alpaslan, F.N. (2011). A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local & Global Similarity and Missing Data Prediction. In: Gelenbe, E., Lent, R., Sakellari, G., Sacan, A., Toroslu, H., Yazici, A. (eds) Computer and Information Sciences. Lecture Notes in Electrical Engineering, vol 62. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9794-1_22
Download citation
DOI: https://doi.org/10.1007/978-90-481-9794-1_22
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-9793-4
Online ISBN: 978-90-481-9794-1
eBook Packages: EngineeringEngineering (R0)