Views
Content related to "hybrid-recommendation"
-
(1)
The evaluation of a hybrid recommender system for recommendation of movies
-
In this paper we present our approach to generation of
movie recommendations. The idea of our hybrid approach is to first
separately generate predicted ratings for movies using the contentbased
and collaborative recommender modules. Predicted ratings
from both recommender engines are then combined into final classification
by the hybrid recommender using weighted voting scheme.
The basis for the calculations are Pearson’s correlation coefficient,
True Bayesian prediction and M5Rules decision rules. The evaluation
of the system performance was based on the EachMovie data
corpus, for around 7000 users. Preliminary results show that this approach
works really well, while there is still some room for improvement.
This site conforms to the following standards:
|
