USERS INTEREST PREDICTION MODEL
Based on 2
nd
Markov Model and Inter-transaction Association Rules
Yonggong Ren
1
and Alma Leora Culén
2
1
School of Computer and Information Technology, Liaoning Normal University, Dalian, China
2
Department of Informatics, University of Oslo, Oslo, Norway
Keywords: Web Data Mining, Inter-transaction Association Rules, Dual-strategy Database, Dual Strategy Users
Interest Prediction Model.
Abstract: The 2
nd
Markov Model and inter-transaction association rules are both known as key technologies for
building user interest prediction models. The use of these technologies potentially improves the users
surfing experience. The use of the 2
nd
Markov Model increases the accuracy of predictions, but it does not
cover all the data. Therefore, in this paper we propose a dual strategy for a user interest prediction model
that includes the entire data set and improves the accuracy of inter-transaction association rules. The
foundation of our dual strategy is a new method of building a database based on the degree of user interest.
Secondly, we integrate the 2
nd
Markov Model and inter-transaction association rules for predicting future
browsing patterns of users. Experimental results show that this method provides more accurate prediction
results than previous similar research.
1 INTRODUCTION
Web data mining may be applied towards helping
users more efficiently acquire needed information
from the huge quantity available on the Internet.
Consequently, web data mining may be used as a
means for improving the web surfing experience for
users. In this paper we propose a model for
predicting users’ surfing patterns. Our model gives
more accurate predictions than any other research
results we have seen.
Much research has been done using the Markov
Model to predict users’ interests. Some examples
relevant to our work in are: (Khalil et al., 2006),
(Chimphlee et al., 2006), (Chimphlee et al., 2006),
all three using both intra-transaction association
rules and the Markov Model, and one that integrates
clustering, intra-transaction association rules, and
the Markov Model (Khalil et al., 2008). (Ren et. al,
2009) clustering algorithm could be used instead of
the one proposed in (Khalil et al., 2008). However,
we are aware of no research that shows how the
construction of the database can affect the accuracy
of prediction and, consequently, the efficiency of
browsing. In this paper we propose a dual strategy,
based on the degree of user interest, to build a
database and then integrate inter-transaction
association rules and the 2
nd
Markov Model. The
model resulting from this includes a new database
construction method and a new prediction method
that we call the Dual-Strategy User Interest
Prediction Model (DUIPM).
2 BACKGROUND
Complete web usage data mining results are
specified as a set of web pages denoted by , where
 =  {

,

…

}, and  is the total number of
pages visited during the mining process. The set of
users is denoted by, where  =  {

,

,…,

}
and the users’ surfing sessions set is denoted by ,
where  = {

,

,…,

}. Each 

is a set of web
pages browsed by user 

during a surfing session.


=
{
(

,

,

),
(


,

,

)
,…,(

, 

,

)
}
. 

is
clearly a subset of .
There are two additional parameters associated
with each

: the time the user spent in session and
the weight associated with each visited page. Let


be the time which user 

spent on the page 

and 

the weight associated with page 

. If


= 1, the user visited the page 

; if 

= 0,
the user did not visit the page 

.
244
Ren Y. and Culén A..
USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules.
DOI: 10.5220/0003659502360241
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (KDIR-2011), pages 236-241
ISBN: 978-989-8425-79-9
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Let 

be a user in . Let  be the frequency with
which the user 

visited some website,  the time 

spent on one surfing session,  the total number of
visits to that website by all users in , and  the
total time that all users spent on the website. If 
and  are large, this may indicate that users like the
website.
The Dual-strategy User Interest Degree (
DUID )
is then defined as follows:
(, 

,)=

∑∑



,=1



.
Using all the users’ surfing sessions to construct
a prediction database, a dual strategy is decided
upon as follows: if (, 

, )  < ,
we use strategy one, else we use strategy two.
Strategies one and two are described in section 3.1.
Based on a series of web pages that a user has
visited, the Markov Model can predict the page the
user will visit next. Let  be a set of pages visited
during one surfing session, sorted by the length of
visiting time. If some user has already visited 
pages, the equation below may be used to calculate
what the next page 

would most likely be.
The equation 

=
(



|

)
=(

=
|

,

,...,

)represents the Markov Model
(MM) for predicting the next page to be visited with
the highest probability.
As can be seen from the equation, as  and 
increase, the probability (

|) also grows, and
thus the resulting prediction becomes more accurate.
But with the larger  and , the calculations become
heavier and consequently the efficiency of the
calculations becomes lower. Therefore we introduce
a parameter  to control the quantity of data. Adding
 leads to the equation known as 
th
-Markov Model:


 = (

=|

,

,...,

(

)
).
The number of sampled web pages is thus
controlled by the parameter; in fact, the prediction
can be made using exactly  pages (see (Chen et al.,
2004) and (Mobasher et al., 2002)).
Looking at association rules and their usage in
data mining, we use the definition of inter-
transaction association rules as given in (Tung et al.,
1999). A transaction in our case is simply a set of
pages visited by a single user, EP extended set of
pages and α confidence.
An implication of the form ⇒ is called an
inter-transaction association rule if
(1) ∈,∈,∩⊆
(2)∃

(
1
)
∈,1≤≤,∃

(

)
∈
, 1 ≤  ≤ ,  ≠ 1
(3)  = ( ∪ )/() ≥ min .
From the papers (Mobasher et al., 2002) and
(Mobasher et al., 2001) we see that these rules give
superior results in comparison with other methods
involving association rules.
The traditional association rules may be
considered as intra-transaction, i.e. they are limited
to associations within the transaction. The difference
between “intra” and “inter” is that while “intra”
remains within the transaction, “inter” can find
relationships across different items, thus breaking
the barrier of a single transaction. In order to further
clarify this distinction, we provide two examples:
one of intra-transaction association rules and one of
inter-transaction association rules.
Example 1. If user 

visits web page A and
then page B, by intra-transaction association rules
we may be able to conclude that a user

, after
visiting page A, will visit page B with a probability
of 0.5.
Example 2. If user 

visits web pages A and B,
and user 

visits web pages A, B and then C, inter-
transaction rules may allow for the conclusion that
user 

will, after visiting page A, visit page C with
a probability of 0.2.
In (Deshpande et al., 2004) selective Markov
Models are discussed. We will use the following
definition: a Markov Model with pruning of
redundant data and data whose frequency is less than
some minimum confidence is called the Frequency
Pruned Markov Model (FPMM).
With these definitions in mind, we are in a
position to discuss our dual-strategy user interest
prediction method.
3 DUAL-STRATEGY USER
INTEREST PREDICTION
METHOD
When the value of  is small in a 
th
-Markov Model,
the model is called the low Markov Model. The
higher the value of  is, the higher the accuracy, but
also the complexity of computations. For this reason,
the low model of sufficient accuracy, and high
efficiency, if combined with a strategy for increasing
accuracy, is to be preferred. One of the main issues
with the low Markov Model is that of data coverage.
Our approach to remedying this problem is to use
inter-transaction association rules and recover some
missing association rules covering the missing data.
This increases the coverage and thus the accuracy,
while it does not increase the complexity of
computation associated with the use of higher order
USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules
245

Markov Models.
The Dual-strategy User Interest Prediction
Method (DUIPM) is then the method that integrates
the 
th
-Markov Model and inter-transaction
association rules for low values of.
For addressing this trade-off between accuracy
and complexity of computation, we use the
frequency pruned Markov Model to pre-process the
data. Referring to (Khalil et al., 2008), accuracy of
the 1
st
to 4
th
FPMM is compared on four different
databases: D1, D2, D3 and D4. Results are shown in
Figure 1 and Table 1. As seen from Figure 1, the 2
nd
FPMM is more accurate than the 1
st
.
Figure 1: The contrast of the 1
st
, 2
nd
, 3
rd
and 4
th
FPMM
based on accuracy (in percentages).
Table 1 shows that the 2
nd
FPMM covers the
data much better than the 1
st
FPMM and is closer to
the 3
rd
and 4
th
. Therefore, we choose the 2
nd
FPMM
for our dual strategy.
Table 1: The data coverage of the 1
st
- 4
th
FPMM.
1-FP 2-FP 3-FP 4-FP
D1 745 9162 14977 17034
D2 502 6032 18121 22954
D3 623 5290 11218 13697
D4 807 7961 19032 23541
3.1 Data Pre-processing
Data from the web log cannot be used directly; part
of the data is redundant, and part is not relevant for
the computations to follow. Thus, pre-processing of
the data is a necessary step in order to increase the
efficiency of the algorithms. Some examples of data
that need to be eliminated include redundant data,
error logs, and graphical, video and audio files.
We now use an example involving four users and
their surfing sessions in order to show how we
construct our Dual-Strategy Database. We show
only the elimination of data by FPMM (redundant
data and low frequency data). Table 2 shows the
original database, which includes the surfing
sessions from four users. The items in each session
are all web pages that a specific user has visited.
Table 3 shows explicitly which pages were visited
by users, in which order, and including the time they
spent on the session. Table 4 provides the frequency
of every web page. From definition of FPMM, the
items whose frequency is less than some minimum
frequency value are pruned.
Table 2: The original database.
A,G,T,A,C,S,G,J,R,A,D,H,M,D,J
F,D,H,N,I,J,E,A,C,D,H,M,I,J,G,M
A,F,I,J,E,C,D,H,N,I,J,G,D,H,N,C,I,J,G,A,N
F,L,S,D,H,N,J,Q,E,I,P,C,I,O,A,D,H,M
A,C,G,A,D,H,M,C,F,C,G,R,I,P,H,O,J
A,I,J,B,A,E,C,T,D,H,M,I,Q,G
A,F,I,B,A,E,D,H,N,P,I,Q,F,J,D,H,N,G,C
F,D,H,M,I,J,E,H,F,I,J,E,D,H,M,A,G,N
F,D,H,N,J,A,D,A,E,D,J,R,H,N,G,C,F,G
A,C,D,E,G,C,A,F,N,H,M
Table 3: Surfing sessions for four users.
User
Session Time


A,F,I,J,E,C,D,H,N,I,J,G,D,H,N,C,I,J,G,A,N 150s
F,D,H,N,I,J,E,A,C,D,H,M,I,J,G,M 300s
F,D,H,M,I,J,E,H,F,I,J,E,D,H,M,A,G,N 120s
A,C,D,E,G,C,A,F,N,H,M 260s


A,C,G,A,D,H,M,C,F,C,G,R,I,P,H,O,J 20s
A,G,T,A,C,S,G,J,R,A,D,H,M,D,J 10s


A,F,I,A,E,D,H,N,I,F,J,DH,N,G,C 40s
A,I,J,A,E,C,D,H,M,I,G 50s
F,D,H,N,J,A,D,A,E,D,J,H,N,G,C,F,G 30s


F,D,H,N,J,E,I,C,I,A,D,M 10s
Table 4: The frequency of each page.
Page A B C D E F G H I
Freq. 18 2 13 18 9 11 13 18 14
J L M N O P Q R S T
15 1 9 11 2 3 3 3 2 2
Assuming that the minimum confidence value is
set to 4, web pages B, L, O, P, Q, R, S and T are
eliminated from the database.
When a user 

visits some web site for the first
time, if the parameters from web log satisfy
(, 

,)<, we use database
strategy 1 to create the database for predicting the
users interest. However, if the parameters from web
log satisfy
(
, 

,
)
> , we use
database strategy 2 to create the database. Thus, this
process of building the database is named Dual-
0
10
20
30
40
50
60
1-FPMM 2-FPMM 3-FPMM 4-FPMM
D1 D2 D3 D4
KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval
246

strategy Database. The result of these two strategies
is shown in Tables 5 and 6. Note that Table 5 has the
first four rows unchanged, as strategy 2 applies to
them. Strategy 1 applies to the remaining rows and
the data in these rows is pruned. Strategy 2 applies
to user 

only, and the result is shown in Table 6,
including time user 

has spent on each session.
Table 5: The database after strategy 1 is applied.
A,G,T,A,C,S,G,J,A,D,H,M,D,J
F,D,H,N,I,J,E,A,C,D,H,M,I,J,G,M
A,F,I,J,E,C,D,H,N,I,J,G,D,H,N,C,I,J,G,A,N
F,S,D,H,N,J,E,I,C,I,A,D,H,M
A,C,G,A,D,H,M,C,F,C,G,I,H,J
A,I,J,A,E,C,D,H,M,I,G
A,F,I,A,E,D,H,N,I,F,J,D,H,N,G,C
F,D,H,M,I,J,E,H,F,I,J,E,D,H,M,A,G,N
F,D,H,N,J,A,D,A,E,D,J,H,N,G,C,F,G
A,C,D,E,G,C,A,F,N,H,M
Table 6: The database after strategy 2 is applied.
User Long User Sessions Time


A,F,I,J,E,C,D,H,N,I,J,G,D,H,N,C,I,J,G,A,N 150s
F,D,H,N,I,J,E,A,C,D,H,M,I,J,G,M 300s
F,D,H,M,I,J,E,H,F,I,J,E,D,H,M,A,G,N 120s
A,C,D,E,G,C,A,F,N,H,M 260s
3.2 Prediction Strategy 1
The 2
nd
FPMM is the first component in our Dual-
Strategy to predict user’s interest. The second
component is Dual-Strategy Database construction.
Algorithm 1 then describes the first strategy for
predicting user’s interest, based on the 2
nd
FPMM
and the Dual-Strategy Database construction
applying the strategy 1.
Algorithm 1:
Input: The original database.
Output: The results of the 2
nd
FPMM
For each 

in 
,
for each 

in T,
for
 = 1and for each 

in S
//Prepare for constructing a data
base by strategy 1
Create (Sum);
//Prepare for constructing a data
base by strategy 2
Create (Sum’);
//Eliminate redundant data with FPMM


’ = FPMM(

);
Add (

’) to Sum;
If 
(
, 

,
)
> ,
Add (

’) to Sum’;
If(, 

, )  < ,
Find the two most frequently
appearing consecutive items
Predict using (Sum);
Else
Predict using (Sum’);
End
3.3 Prediction Strategy 2
Data coverage is, as mentioned above, one of the
main shortcomings of the 2
nd
FPMM, causing the
inaccuracy in predicting user’s interest. This issue is
remedied by integrating inter-transaction association
rules. A better prediction of the next web pages that
the user may visit is obtained by using combination
of inter-transaction rules and the 2
nd
FPMM.
Algorithm 2:
Input: Dual-strategy Database
(processed by Algorithm 1).
Output: The web pages most likely to
be visited next.
When (database
≠
null)
Set U as a vector;
//Using 2
nd
FPMM, find the two most
frequent consecutive items
U = Find_2ndMM(2, line);
//Find their next occurrence
Find_continious(U);
//Get probability P
i
for every next
item
P
i
=Get_probability(Find_continuous(U
));
If(P
1
= P2 = ... = P
n
) {
//Extract items from the original
database up to the first occurrence
of most frequent continuous items
and items between the two
occurrences of the most frequent
continuous items in order to find
inter-transaction associations.
New_database(Begin_of_transaction
After_continious,Before_continious);
Find_iter_transaction_association_
rules (New_database);
Make_prediction(A,B, C ⇒ D);}
If(P
1
> P
2
> ... > P
n
) {
//Record the page with the largest
probability(P
max
)
Remember(Page(P
1
))；
New_database(Begin_of_transaction
After_continious,Before_continious);
Find_iter_transaction_association_
rules (New_database);
//Recommend the page which has P
max
//Recommend the results of rules
Make_prediction(Page(P
1
));
Make_prediction(A,B, C ⇒ D);}
End
USERS INTEREST PREDICTION MODEL - Based on 2nd Markov Model and Inter-transaction Association Rules
247

20
25
30
35
40
45
50
D1 D2
2nd MM DUIPM 2nd FPMM
4 EXPERIMENTAL RESULTS
4.1 Experimental Data
After data pre-processing, the strategy to use is
determined by the DUID. Sessions by users 

,

and 

satisfy the equation (, 

,)<
, thus the strategy 1 is used, while


does not satisfy the equation and therefore the
strategy 2 is used (as shown in Tables 5 and 6,
respectively). The two consecutive web pages which
appear most frequently in the resulting database are I
and J. Thus, the probabilities that I and J are
followed by E and G, respectively, are:


= {(|, )} = { = 0.57} ,


={(|,)}={=0.43} .
The probability of E being the next page is larger
than that of G. There still may be some prediction
results that algorithm 1 could not uncover, so we use
algorithm 2 to find them. Using algorithm 2, a new
association rules based database is created and used
to predict the next pages.
Table 7: The predictions for user 

after visiting pages
I and J.
Part of session 2
n
d
FPMM Prediction
A,F I,
J
E
C,D,H,N I,
J
G
D,H,N,C I,
J
G
F,D,H,N I,
J
E
A,C,D,H,M I,
J
G
F,D,H,M I,
J
E
H,F I,
J
E
The association rule from this database is thus
⇒,,. The prediction result for user 

can be
E (most likely), then G (see Table 7). If web page D
appears in this session, by association rules, the
prediction can be E and J.
When a new user visits this web site, the
prediction can be made by building database using
strategy 1. The results are as follows:


= 
{

(

|
, 
)
=0.5
}
,


= 
{

(

|
, 
)
=0.5
}
.
Thus, the probabilities of both M and N are 0.5.
The inter-transaction association rule in this case
is ,  ⇒ , . As before, if a new user visits pages J
and I, then we can predict M and E as the next most
likely pages to be visited. If the user does not visit
pages J and I, pages M and N are most likely web
pages to be visited next (see Table 8).
Table 8: The predictions for user 

after visiting pages
D and H.
Part of session 2
n
d
FPMM Prediction
A,F,I,J,E,C D,H
N
I,J,G D,H
N
FD,H
N
I,J,E,A,C D,H M
FD,H M
I,J,E,H,F,I,J,E D,H M
A,C,G,A D,H M
A,G,A,C,G,J,A D,H M
A,F,I,A,E D,H
N
I,F,
J
D,H
N
A,I,J,A,E,C D,H M
FD,H
N
FD,H
N
J,E,I,C,I,A D,H M
4.2 The Performance of the Model
In Figures 2 and 3 we compare different algorithms:
the 2
nd
Markov Model (2
nd
MM), the 2
nd
Frequency
Pruned Markov Model (2
nd
FPMM), and our Dual
User Interest Prediction Model (DUIPM).
Figure 2: Accuracy based comparison between the 2
nd
MM, DUIPM and 2
nd
FPMM.
The 2
nd
MM and the 2
nd
FPMM use databases
created by strategy 1, while the use of strategy 2 is
specific for DUIPM. The 2
nd
FPMM prunes
redundant data, so its prediction results are
somewhat more accurate than the ones using only
the 2
nd
MM. The DUIPM is clearly the most
accurate method, due to the integration of inter-
transaction association rules in strategy 2. Figure 2
shows the accuracy based comparison.
On the other hand, DUIPM is much more
complex than the 2nd FPMM and the 2nd MM. It
uses two kinds of database construction methods and
integrates inter-transaction association rules, so its
performance is the lowest. 2nd FPMM has a pruning
strategy, so its performance is higher than the 2nd-
MM’s, as shown in Figure 3.
KDIR 2011 - International Conference on Knowledge Discovery and Information Retrieval
248

Figure 3: The performance based comparison of the three
models.
In summary, the performance of DUIPM is the
weakest, but computational power is increasing all
the time and it may be justified to sacrifice some
performance in favour of better prediction accuracy.
5 CONCLUSIONS
Based on the two methods for database building,
integration of the 2
nd
FPMM and inter-transaction
association rules, we propose a different model for
users’ interest prediction. The database building
method and the use of inter-transaction association
rules offsets the main disadvantage of the 2
nd
Markov Model - that it cannot cover enough data to
make accurate predictions. As future work, we
would concentrate on improving the performance of
the algorithm and providing additional experimental
results.
ACKNOWLEDGEMENTS
This research was supported by Science and
Technology Plan Projects of Liaoning Province
Grant No. 2008216014), Science Foundation for the
Excellent Youth Scholars of Dalian (Grant No.
2008J23JH026), Liaoning Educational Committee
(Grant No. L2010229) and SRF for ROCS, SEM.
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