Open Journal of Social Sciences, 2014, 2, 5-11
Published Online July 2014 in SciRes.
How to cite this paper: Tung, W.-F. (2014) Group Ranking Sequence Decision for Recommendation of Messaging APP. Open
Journal of Social Sciences, 2, 5-11.
Group Ranking Sequence Decision for
Recommendation of Messaging APP
Wei-Feng Tung
Department of Information Management, Fu-Jen Catholic University, New Taipei City, Chinese Taipei
Received May 2014
This researc h is to develop a novel recommendation service using a unique group ranking se-
quence technique “Mining Maximum Consensus Sequences from all Users Partial Ranking Lists
(MCSP). MCSP is capable of determining the products sequence recommendations based on
k-item candidate sequences and maximum consensus sequences. This paper also illustrates the
complete decision procedures of group ranking sequences. In terms of popular information prod-
ucts, we select “messaging appto reveal the MCSPs group ranking sequence decision. The rec-
ommendation service provides that query users search for the products recommendation (i.e.,
messaging app) according to the preference sequences from query users themselves and a great
deal of preference sequences from the other users. This paper consists of the definitions, proce-
dures, impleme nt ation, and experiment analysis, as well as system demonstrations of MCSP re-
spectively. This rese arch contributes to a kind of systematic service inno vation.
MCSP, Group Ranking Sequences, Recommendation Service, Messaging APP
1. Introduction
Decision support systems have developed for a long time. However, the decision problem is still an important
issue for varied business applications [1]-[3]. A group-ranking-based decision methodology is capable of rank-
ing products preference sequences between group consensus sequences and group conflict sequences [4] [5]. All
user preference sequences can be used to estimate the possibility of being candidate sequences and further
measure the levels of consensus and conflicts to determine the maximum consensus sequences for the product’s
recommendation (Figure 1). This research also presents the experiments to compare the differences of MCSP’s
recommendation as well as users’ feedbacks. In addition, we demonstrate the system platform to show the rec-
ommendation results. Comparing with various recommendation techniques, MCSP offers a unique capability
that can deal with the complex sequential data rather independent objects. Furthermore, this research also im-
plements MCSP to be a service system that can be used to enforce the group ranking decision for recommenda-
tion of messaging app while the query users input their preference sequences. Such online service can conduct a
W.-F. Tung
Figure 1. MCSP s procedures for group ranking decision.
group ranking decision technique for varied product recommendations. MCSP also can be developed to be a de-
cision support system (DSS) for further Web 2.0 applications. MCSP can be applied to estimate varied products
or brand [6]-[9]. For the service innovation research, this research also can enhance the service innovation of the
virtual community [4] [5] [10]-[15].
MCSP Implementation
A k-item sequence includes 1 to N items sequence in a dataset. Ck would be a set of candidates with k-sequence.
Ck is a set of k-sequence that reaches to the consensus threshold. The beginning stage is to search for a consen-
sus ranking. In order for the ranking majority users agree, the algorithm is to estimate the cmp_sup (complying
support) and cf_sup (conflict support) of the candidate sequence Cs. While cmp_minsup = 0.3 and cf_maxsup =
0.25, the consensus sequence reaches to
( )
( )
supsup cs
cmp0.3and cf_supsupcs0.25≥≤
, the sequence will be in-
cluded into theset of Lk-1, which also can be included into the set of candidate sequence Ck. When all se-
quences in Lk-1 cannot generate the k-sequence anymore, the highest consensus ranking should be deter mined.
Both predefined values for cmp_minsup and cf_maxsup will make the difference of recommendation results.
If the two thresholds are too high, the sequence results will be fewer than the low thresholds. In this study, we
predefine the two thresholds (cmp_minsup = 0.3, cf_maxs up = 0.25) and also can be adjusted depend on the
different problems. While the dataset includes 14 sequences, S represents the sequential number of users and S1,
2 means the second sequence of User1 (Table 1).
The first procedure is to divide the sample data into all 2-item sequences to be a set of candidate sequences C2
(Table 2).
In order for the first candidate sequence generate, all sequences will be selected from set I. All items can be
selected for ranking to be L1 = {A, B, C, D, E, F}.
can compare all pairs of L1 and then generate the
sequence C2 (Table 3). According to equation 2 to 8, all the sequences in C2 (Si, j) and compare with the
cmp_sup and cf_sup of user preference sequences. All sequences include {A > C}, {A > D}, {A > E}, {B > C},
{B > D}, {B = F}, {C > D}, {F > A}, {F > C}, {F > D}, and {F > E}, which will be selected into the set of
consensus sequence L2.
The 3-itme sequences list includes the two consensus sequences, the 2-items sequences would be deleted
without fitting the thresholds. In L2, the 2-items sequences, {B > D}{B > C} {B = F}, won’t be select into the
other more k-item sequence anymore. Therefore, the three sequences become the highest consensus sequence.
The next process is for determining 3-item sequences, and the process is the same with 2-item sequence. It
computes all candidate sequences in C3 and [cmp_sup, cf_sup] of user preference (Si, j). According to Equation
(6), the set of L3 involves {F > A > C} , {F > A > D}, {F > A > E}, and {F > C > D} which fits the 3-itme thre-
W.-F. Tung
Table 1. Sample data of 6 users and 14 sequences.
User ID Si,1 Si,2 Si,3
u1 B = F > A > C F > A > D > E
u2 F > C > A = B > D
A > B = D > E F > A > D > E
u3 C > B = F > D C > B = F > A > E
u4 A > B F > C > E B = F > C > D
u5 F > A > C > D > E F > A = B > C > D
u6 F > A > B C F > A = B > C F A > C > D
Table 2. All 2-item sequences in the set of C2.
A > B B > A C > A D > A E > A F > A
A = B B = A C = A D = A E = A F = A
… … … … … …
A > F B > F C > F D > F E > F F > E
A = F B = F C = F D = F E = F F = E
Table 3. 2 -item candidate sequence.
W.-F. Tung
In Table 4, {F > A > C} and {F > C > D} can combine to 4-itme {F > A > C > D}. The two sequences just fit
for S5, 1 and S5, 2. As for conflict sequence, any sequence related to {F > A > C} or {F > C > D} need to be es-
timated if they meet conflict. The sequence will not to recommend if one of sequence meets conflict. Thus, the
process needs to present all unions of sequence.
Table 4. 3-item candidate set.
Table 5. Consensus sequence with 4-item.
S1 User Num
F > A > C
1 {1} {} 2
2 {} {1} 3
3 {} {2} 2
4 {} {1} 2
5 {1, 2} {} 2
6 {1, 2} {} 3
S2 User Num
F > C > D
1 {} {} 2
2 {1} {} 3
3 {} {1} 2
4 {2} {} 2
5 {1, 2} {} 2
6 {3} {} 3
Candidate Sequence, C User Num
F > A > C > D
1 {} {} 2
2 {} {1} 3
3 {} {1, 2} 2
4 {} {1} 2
5 {1, 2} {} 2
6 {} {} 3
W.-F. Tung
Table 6. Max consensus sequences.
Max Consensus Sequences
No of
cmp_sup cf_sup
LIN E > We Chat {B > C} 0.39 0.22
LIN E > Wha t s AP P {B > D} 0.31 0.06
LIN E = Skype {B = F} 0.33 0.25
Skype > QQ > We Chat {F > A > C} 0.36 0.14
Skype > QQ > Wha t sAP P {F > A > D} 0.36 0.00
Skype > QQ > Viber {F > A > E} 0.31 0.08
Skype > We Chat > Wh a t sAP P {F > C > D} 0.36 0.08
Table 7. The experiment analysis.
Since {F > A > C > D} does not meet the threshold, so it is not able to list for the consensus sequences. The
four 3-item sequences then can be listed into the max consensus sequence {F > A > C}, {F > A > D}, {F > A >
E}, and {F > C > D}, because no one else reach the set of consensus sequence.
2. Experiment Analysis
As the algorithm of group ranking (MCSP) for group recommendations is based on the other users’ preference
sequences, the experiments show the comparisons of L2 and L3 based on the number of users. When MCSP
provides the results of group ranking, the more number of users, the fewer number of consensus sequences. The
more number of users, the more number of conflict sequences in this experiments.
In addition, this research tests our sample users to answer the questions about the system usages. From the
user feed backs of Questionna ir es, the research proposes some critical implications. The analysis unfolds that the
more maximum consensus sequences and level of averaged user satisfactions will be verified if the more num-
bers of preference sequences users provided. On the other hand, the fewer preference sequences made the less
maximum consensus and the levels of user satisfactions (Table 8).
3. System Demonstration
In order to demonstrate the functions of MCSP, the research implements a web-based system on the Internet that
can really provide the query users to input their preference sequences and get the group ranking decision. MCSP
W.-F. Tung
is used to develop a product recommendation service in messaging app in this study. When the query users input
their preference sequences, the Web-based system platform can generate some group ranking sequences for
query users. The input and get of the prototype system of conducting group ranking sequences are shown in
Figure 2 and Figure 3.
4. Conclusion
This research utilizes a unique technique of group ranking sequences decision Mining Maximum Consensus
Sequences from all UsersPartial Ranking Lists (MCSP)to develop an innovative online recommendation ser-
Table 8. Averaged levels of satisfactions depends on the number of pr e f e r ence
sequences and maximum consensus sequences.
12 sample data All sample data
1 4 4
u3 4.50 4
u6 4.50 4
u7 2 1
u10 3 4
u11 2.50 3
u13 4.50 4
3.57 3.43
12 sample data All sample data
u2 2.50 3
u4 3.50 2
u5 4 3
u8 3 2
u9 2.50 3
u12 2 3
u14 3.50 4
AVG_ satisfaction 3 2.86
Figure 2. A group ranking sequence result.
W.-F. Tung
Figure 3 . A group ranking sequence result.
vice of messaging app of mobile phones. This paper consists of the definitions, procedures, i mpleme ntatio n, and
experiment analysis, as well as system demonstrations of MCSP respectively. The section of MCSP implemen-
tation shows how MCSP conducts a decision process of determining group ranking sequence. The section of
experiment analysis is to verify the effects of MCSP. Finally, a prototype system can demonstrate input and
output of recommendation service of messaging app. However, this research still has some research limitations
of the number of sequences and the time complexity.
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