![](bg3.png)
equation (3.3) is used in which the weight, w
i
, and
the confidence values are used to find the value of
user interestingness, I (α
i
, B, ξ).
User Interestingness: I (α
i
, B, ξ) = d (α
i
| α
i
’, ξ) -
d (α
i
| ξ) = ǀ Confidence (α
i
’) – Confidence
(α
i
) ǀ = ǀ P (E | α, ξ) - P (α | ξ) ǀ (3.3)
A new degree of belief denoted as P (α| E, ξ) in
which the belief, α is based on the new evidence E in
the context of the old evidence ξ. It can be computed
using Bayes’ rule, as given in equation (3.4).
New degree of Belief: P (α | E, ξ) = P (E |α, ξ) P
(α | ξ) / {P (E | α, ξ) P (α | ξ) + P (E|
¬
α, ξ)
P (
(
¬
α | ξ) (3.4)
An interestingness of pattern p, relative to
previous evidence ξ can be determined by P (α | p, ξ)
whereby it represents the confidence of rule p, given
belief α as in equation (3.5). It also can be defined as
the user interestingness relative to the difference
between the prior and posterior probabilities in the
belief system.
User Interestingness measurement pattern p: I
(p, B, ξ) =
∑
{| P (α | p, ξ) – P (α | ξ) |}
/ P (α | ξ) (3.5)
By taking the confidence values as the initial
degree of belief in a belief system, a new pattern
using user interestingness values, I (α
i
, B, ξ) will be
generated and the patterns for pre-fetching set and
candidate set will be compared and analysed.
Let us consider Table 1 as a sample of online
customer order information. In this example, we
want to show that the pre-fetched items or products
using the integrated measurement really meaningful
for the user.
First, we generate the list of products from Table
1 for the candidate set using a-priori algorithm. We
specify the minimum support value (min_sup) = 4
and the minimum confidence value (min_conf) =
0.5, to produce the list of strong association rules as
in Table 2. A list consists of the most frequently
ordered products which has been filtered using
support and confidence metrics. In this case, the
objective measurement in which the data
interestingness of the products has been considered.
From Table 2, the highest confidence value is for
products
B#3→B#4, i.e. B#3 and B#4. Then it
followed by B#4 and B#5 and so on. It means that
these are the products which are the most frequently
ordered products by the customer. Based on data
interestingness, these are the products that will be
pre-fetched as the pre- fetching set. This approach
has been used in the previous work
Then we extend the previous work by
introducing our approach called integrated
measurement.
After the objective measurement process has been
carried out, a subjective measurement is introduced
to filter according to the user interestingness
products. After the products from the strong rules
are treated for the candidate set, we then treat the
highest confidence value as the initial belief of user
in a belief system for the transaction. This belief is
important in determining the future products to be
ordered by the user. The initial belief represents
general knowledge of ordering behaviour of the
user.
By using equation (3.1) to (3.5), we compute the
results to determine the pre-fetching set from strong
rules as in Table 3. It consists of nine beliefs, in
which support is used to calculate weights for non-
bias values and confidence value is treated as the
initial confidence for each belief.
Table 1: Sample of a Customer Order information.
Cust
_ID
Order_Date Product_Query
Cust
_ID
Order_Date Product_Query
CO1
2-May-07 B#6,B#8,B#16
CO1
3-October-08 B#12
26-May-07 B#4,B#10,B#15,B#20,B#5,B#8 10-November-08 B#2
2-Jun -07 B#2,B#5,B#3 29-Disember-08 B#8,B#6,B#5,B#4
6-Jul-07 B#6,B#9,B#5,B#4,B#25,B#10 27-Jan-09 B#4,B#12,B#6
3-Jan-08 B#2,B#20,B#6 5-Feb-09 B#3
12-Apr-08 B#5,B#4,B#6,B#3,B#15,B#16 12-Apr-09 B#20,B#2,B#8
9-May-08 B#8,B#2,B#15 15-May-09 B#2,B#1
7-Jun-08 B#4,B#25,B#6,B#5 15-Jun-09 B#8,B#3,B#4,B#5,B#2,B#16,B#5
19-July-08 B#16,B#12,B#4,B#20,B#10 11-July-09 B#9
1-August-
08 B#8,B#4,B#15,B#3,B#12,B#2 14-August-09 B#9,B#20,B#4,B#3,B#15,B#1
15-Sept-08 B#8,B#25,B#3,B#4,B#5,B#20 15-September-09 B#9,B#8,B#3,B#5,B#4,B#15
IntegratedMeasurementforPre-FetchinginMobileEnvironment
207