M. Sadeghi, S. A. Asghari
410
Figure 2. The incorporation of context in the recommendation process [13].
algorithm. The important benefit of this approach is that it allowed using any
recommender system technique. The pre-filtering approach used two-dimen-
sional (users × items) recommendation to estimate the rating function [15] [16].
In post-filtering approaches, the result list of the recommendation is prepared
and then filtered with contextual information as shown in
Figure 2(b). The rat-
ings are predicted using two-dimensional (2D) recommender system.
Contextual modeling approaches use explicitly the context information as a
predictor for user’s rating an item. Hence, the approach formulates a multidi-
mensional or MD (user × items × context) recommendation as shown in
Figure
2(c) [17] [18] [19] [20].
4. Evolutionary Computing
As shown in Figure 3 Evolutionary Computing (EC) with Fuzzy sets, Artificial
Neural Networks (ANNs), Swarm Intelligence (SI) and Artificial Immune Sys-
tems are subsets of computational intelligence [9].
The root of EC is back from Darwin theory of natural selection. Darwin’s
theory explains that nature has limited resources [21] [22] [23]. Creatures that
live are competing together because of limited resources. And try to extend their
next generation. This generation is called survivor of fittest [24] [25] [26].
Table
1 shows the mapping between this theory and problem solving in the evolutio-
nary computing [23].
In general, every problem has three parts: Input, Model and Output.
EC can solve the problem in three ways.
4.1. Optimization
As shown in Figure 4(a), in these methods input is unknown. Input should
found in a way that it became optimize. Most of the problems are in this category.
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