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

2010-09-22
Özbal, Gözde
Kahraman, Hilal
Alpaslan, Ferda Nur
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.
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.