Computational Intelligence - February 2015 - 59

14

14
MOEA-ProbS
ProbS+HeatS
CF
MF

Novelty

10
8

10

6

6
4

2

2
0

0.05

0.10

0.15 0.20
Accuracy
(a)

0.25

0

0.30

14

0.1

0.2
0.3
Accuracy
(b)

0.4

0.5

8

MOEA-ProbS
ProbS+HeatS
CF
MF

0.7
0.6
Coverage

10

6
4

0.5
0.4
0.3
0.2

2
0

0

0.8
MOEA-ProbS
ProbS+HeatS
CF
MF

12

Novelty

8

4

0

MOEA-ProbS
ProbS+HeatS
CF
MF

12

Novelty

12

0.1
0

0.1

0.2
Accuracy
(c)

0.3

0.4

0
0.1

0.2

0.3
0.4
Accuracy
(d)

0.5

0.6

Figure 9 Final non-dominated solutions of CF, MF, MOEA-ProbS and ProbS+HeatS in the accuracy-novelty space (a) Movielens 1. (b) Movielens
2. (c) Movielens 3. (d) Movielens 4.

Novelty

generate multiple recommendations with
In the experiments, three performance
hybrid recommendation algorithm [16]
higher coverage and similar accuracy
metrics are considered, namely, accuracy,
is selected as a comparative algorithm,
compared to ProbS+HeatS. This is a
coverage and novelty. As displayed in Figs.
which combines an accuracy-based
promising property, especially for online
8 and 9, the solutions of CF and MF are
method (ProbS) and a diversity-focused
business. Diverse items can be discovered
dominated by those of MOEA-ProbS on
method (HeatS). For convenience, the
to stimulate the purchase desire of cusall the data sets, which demonstrates the
hybr id algor ithm is denoted by
tomers. However, MOEA-ProbS is beateffectiveness of our algorithm. Fig. 8
ProbS+HeatS. A weight parameter
en by ProbS+HeatS according to the
shows that MOEA-ProbS is able to
m ! [0, 1] is used to incorporate these
accuracy metric. The reason is
two algorithms with completetwofold. First, the performance
ly different features. Different
of our algorithm is mainly infrom searching for one optimal
MOEA-ProbS
fluenced by the introduced acm through extensive experiProbS+HeatS
curacy-based recommendation
ments in [16], we generate a
technique. Hybrid recommennumber of m evenly sampled in
dation methods, which have
60, 1@ . Then the experiments
10
been proved to provide more
with different m are conducted
8
accurate recommendations [29],
to get a set of recommenda6
can be employed in our model.
tions. A non-dominated front
4
Second, the large-scale search
can be obtained by eliminating
2
space may cause difficulty. In
the dominated points in the ac0
0
0
order to improve the search
curacy-diversity or accuracy0.1 0.2
0.1
0.2 Accuracy
ability, MOEAs should be elabnovelty space. For f air
Coverage 0.3 0.4
0.5 0.3
orately designed and some local
comparison, the number of difsearch methods can be taken
ferent m is equal to the size of Figure 10 Final non-dominated solutions of MOEA-ProbS and
population used in our MOEA. ProbS+HeatS in the accuracy-novelty-coverage space on Movielens 1. into account. According to the

february 2015 | Ieee ComputatIonal IntellIgenCe magazIne

59



Table of Contents for the Digital Edition of Computational Intelligence - February 2015

Computational Intelligence - February 2015 - Cover1
Computational Intelligence - February 2015 - Cover2
Computational Intelligence - February 2015 - 1
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