A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local Global Similarity and Missing Data Prediction

Date

2010-09-22

Author

Özbal, Gözde
Kahraman, Hilal
Alpaslan, Ferda Nur

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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.

Subject Keywords

Collaborative filtering, Floyd warshall algorithm, Pearson correlation coefficient, Recommender systems

URI

https://hdl.handle.net/11511/73098

DOI

https://doi.org/10.1007/978-90-481-9794-1_22

Conference Name

25th International Symposium on Computer and Information Sciences, ISCIS 2010, (22 - 24 Eylül 2010)

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

G. Özbal, H. Kahraman, and F. N. Alpaslan, “A Content Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local Global Similarity and Missing Data Prediction,” presented at the 25th International Symposium on Computer and Information Sciences, ISCIS 2010, (22 - 24 Eylül 2010), London; United Kingdom, 2010, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/73098.