چکیده (انگلیسی):

The rating matrix of a personalized recommendation system
contains a high percentage of unknown rating scores which lowers the quality
of the prediction. Besides, during data streaming into memory, some rating
scores are misplaced from its appropriate cell in the rating matrix which also
decrease the quality of the prediction. The singular value decomposition
algorithm predicts the unknown rating scores based on the relation between
the implicit feedback of both users and items, but exploiting neither the user
similarity nor item similarity which leads to low accuracy predictions. There
are several factorization methods used in improving the prediction
performance of the collaborative filtering technique such as baseline, matrix
factorization, neighbour-base. However, the prediction performance of the
collaborative filtering using factorization methods is still low while baseline
and neighbours-base have limitations in terms of over fitting. Therefore, this
paper proposes Ensemble Divide and Conquer (EDC) approach for solving 2
main problems which are the data sparsity and the rating scores’ deviation
(misplace). The EDC approach is founded by the Singular Value
Decomposition (SVD) algorithm which extracts the relationship between the
latent feedback of users and the latent feedback of the items. Furthermore,
this paper addresses the scale of rating scores as a sub problem which effect
on the rank approximation among the users’ features. The latent feedback of
the users and items are also SVD factors. The results using the EDC approach
are more accurate than collaborative filtering and existing methods of matrix
factorization namely SVD, baseline, matrix factorization and neighboursbase.
This indicates the significance of the latent feedback of both users and
items against the different factorization features in improving the prediction
accuracy of the collaborative filtering technique.