=Paper= {{Paper |id=None |storemode=property |title=An Interest-based Offer Evaluation System for Semantic Matchmakers |pdfUrl=https://ceur-ws.org/Vol-667/smr22010_submission_7.pdf |volume=Vol-667 |dblpUrl=https://dblp.org/rec/conf/semweb/SadaouiJ10 }} ==An Interest-based Offer Evaluation System for Semantic Matchmakers== https://ceur-ws.org/Vol-667/smr22010_submission_7.pdf

An Inter est-based Offer Evaluation System for
Semantic Matchmaker s

Samira Sadaoui and Wei Jiang

Computer Science Department,
University of Regina, Regina, SK, Canada S4S 0A2

{sadaouis, jiang20w}@uregina.ca

Abstr act. Matchmaking systems failed to provide the best matched results to
individuals. Semantic matchmaking can help the buyer find the requested offers
but it is not good enough to find the best offer. In this work, we propose a
system that evaluates and sorts the request-matched offers according to the
buyers’ interests and tastes. To evaluate the offers, we modify the MultiNomial
Logit model to produce an interest model that analyzes individual’s interests
and favors. Our system captures the buyer’s interests, builds his interest model,
and then returns the best offer. The best offer denotes the highest interest value
to the buyer. Through a case study, we present in detail the phases of our offer
evaluation process.

Keywor ds: Interest model, multiple offer attributes, best matched offer, Self
Organizing Map (SOM), MultiNomial Logit (MNL).

1 Intr oduction
Matchmaking is the online process through which buyers and sellers trade goods or
services. Most of the matchmaking systems are semantic-based. Research on ontology
lead the early semantic matchmaking systems to understand and process the
purchasing requests much better [1, 2, 3]. Nevertheless, with the blooming of e-
commerce and e-services, buyers can obtain more and more request-matched offers. It
is really time consuming for buyers to browse, evaluate and sort all the candidate
offers in order to find the best offer. Today determining the best offer is more
important than before for any matchmaker. Recent matchmakers are trying to
determine the best offer by using the semantic ranking [4, 5, 6, 7, 8]. Most semantic
ranking algorithms examine the similarity of inputs, outputs, preconditions and effects.
Yet all existing matchmakers failed to bring the best matched results to individuals.
Indeed, they do no guarantee that the best offer will be purchased by the buyer since
his specific interests and tastes are ignored. Without studying human interests and
only relaying on the semantic matching and ranking, matchmakers cannot recognize
the differences between buyers’ favors and needs.
Researchers realized that a better matchmaking system “could quicken the trend
toward personalization” [9]. Matchmaking based on semantic can help the buyer find
2 Samira Sadaoui and Wei Jiang

the requested offers but it is not good enough to find the best offer. Consequently, we
develop a system that evaluates and sorts the request-matched offers according to the
buyers’ specific interests and tastes. In our system, the best offer denotes the highest
interest value to the buyer. Analyzing individual interests along with the semantic
matching brings better results to each individual buyer. As illustrated in Figure 1, first
the buyer submits to our system a purchasing request which is then sent to a
connected semantic matchmaker. The latter returns a list of request-matched offers.
To sort these offers according to the buyer’s interests, our system performs the
following tasks: cluster the values of each offer attribute, interact with the buyer to
take into account his interests and tastes, calculate the attribute’s interest weight and
interest rate, build the interest model based on interest weights and rates, evaluate and
sort the offers according to the buyer’s interest model.

Fig. 1. System Process Overview

In order to take into account individual’s interests, we need to utilize the clustering
technology called Self Organizing Map (SOM) [10, 11]. As a neural-network
approach, SOM is employed to cluster high-dimensional inputs onto lower-
dimensional outputs. The reason of using SOM in our work is that the offer attributes
may be complex or contain high-dimensional data, such as the attributes of our case
study. Furthermore, to define the interest model specifically for each buyer, we
modify the MultiNomial Logit (MNL) model [12]. MNL is widely used in commerce
to study human shopping behaviors [13]. Nevertheless, MNL suggests a model for a
group of people and needs an appropriate sample data. The good thing is that we are
able to modify MNL to analyze individual’s needs alone and without considering a
sample data.

2 Related Wor k
In the early time of e-commerce, matchmakers focused on mapping the offer attribute
values [14]. Requests and offers can be expressed in different schemas and words
An Interest-based Offer Evaluation System for
Semantic Matchmakers 3
even when representing the same semantic meaning. This causes matchmakers to be
blind to some potential offers. To solve this problem several semantic matching
models, based on ontological technologies, have been proposed [1, 2, 3]. In [1],
matching the offers is based on the similarity of the request and services. [2] uses
logical relationships to map the offers with the request. [3] focuses on the semantic
matching using a platform-independent framework called UDDI. These matchmakers
return an unranked list of offers.
To find the best offer, recent matchmakers evaluate service description [4, 5],
service constraints [6], service process [7] or both [8]. The best offer denotes the
highest semantic matching degree. These matchmakers map the functional properties
of offers with the request’s functional description. However, these matchmakers
cannot explain why sometimes a buyer prefers an offer different from the returned
best offer. To address this issue, non-functional matching methods [15, 16] have been
introduced by following a matching standard like Qos [17]. These papers argue that
the buyer’s choice is caused by other criteria often referred to as non-functional
properties.
All these matchmakers are based on matching the query words but not on matching
individual’s interests. Our goal is to build the interest model specifically for the buyer
and then find the best matched offer which is the closest to the buyer’s real needs.

3 An Example
Our case study consists of purchasing computers based on 2-dimensional attributes:
CPU and Price. We have here a company which submits the following inventory
query:

Request computers with CPU ＞ 1.5 GHz, Price < $3500 for the first ten
purchased computers, and Price ＜ $3000 for the next ten.

To formulate the requests and offers, matchmakers utilize well known web service
languages, such as WSDL, SWSL and WSML, which offer a high degree of
flexibility and expressiveness. Thus, we translate the purchasing request to for
example WSDL following the structure defined in [18] (cf. Figure 2). We assume the
connected semantic matchmaker returns the candidate offers given in Table 1 where
CPU contains high dimensional values and Price has two ranges.
In the next phases, our system will evaluate all the candidate offers of Table 1 in
order to help the company find the best supplier which might become its long-term
business partner.
4 Samira Sadaoui and Wei Jiang

Fig. 2. Formatting the Purchasing Request

Table 1. Candidate Offers

Supplier ID CPU (GHz) PriceRange1 ($) PriceRange2 ($)
For the First 10 Items For Item 11 to 20
1 2.5 1100 799
2 2.2 470 370
3 2.5 600 500
4 2.33 1100 800
5 2.4 999 799
6 2.5 1030 830
7 2.66 2500 2200
8 2.3 1000 880
9 2.2 420 400
10 2.4 950 900
11 2.8 1200 1150
12 (1.9, 1.9) 800 700
13 (3.0, 3.0) 2900 2600
14 (1.8, 1.8) 680 680
15 (3.2, 3.2) 3200 2500
An Interest-based Offer Evaluation System for
Semantic Matchmakers 5

4 Attr ibute Data Cluster ing
Our system first determines all the attributes from the purchasing request. For each
attribute, it extracts all its values from the candidate offers and stores them in a single
table. Our system can now cluster the values of each attribute. The purpose of this
clustering is to be able to take into account the buyer’s interests. So the buyer can
select one of the clustering to represent his most interested area. In Figure 3, we
define the clustering function called ClusterAttributeData() which is based on the
algorithm Self Organizing Map (SOM) [11] given in Figure 4.
We apply SOM to recursively divide a large clustering into three sub-clustering
until there are less data in the clustering. During the learning time, a set of Learning
Vector Quantizations (LVQs) is tuned towards the input attribute data. SOM applies
competitive-learning given in the steps 3.1.1 and 3.1.2 of Figure 4. In SOM function:
t is the learning time; CreateLVQ() is a function that generates three random LVQ mi
in the range of DataAttribute; alpha (t) controls the learning loop and is the learning
rate function which is decreased by learning time t; mc is the closest LVQ to the
selected data x; hci() is the “neighborhood” function that updates LVQ in the learning
time [11].
void ClusterAttributeData(DataAttribute: Array)
{1. SOM(DataAttribute, A1, A2, A3);
//Cluster all data into 3 groups
2. ArrangeClustering(A1, A2, A3);
//Arrange clustering in ascending order
3. for i = 1 to 3
if (IsLargeEnough(Ai))
ClusterAttributeData(Ai);
//Cluster each sub-group}
Fig. 3. Clustering Algorithm of Attribute Data

void SOM(DataAttribute: Array, A1: Array, A2: Array,
A3: Array)
{1. var t = 1; //Initialize learning time
2. CreateLVQ(DataAttribute, m1,m2,m3);//Create LVQ mi
3. while (alpha(t) is not too small)//Decrease by time
{3.1 while (PickUpValue (DataAttribute, x))
//Select x in the data set
{ 3.1.1 ||x - mc|| = min{||x - mi||};
//Find closest LVQ mc
3.1.2 mi(t+1) = mi(t) + hci(t)[x(t)-mi(t)];
//Update mi during learning
}
3.2 t = t+1;//Update time
}}
Fig. 4. SOM Algorithm
6 Samira Sadaoui and Wei Jiang

Example. Figures 5 and 6 illustrate respectively the CPU and Price clustering
(represented as a tree structure).

Fig. 5. CPU Data Clustering

Fig. 6. Price Data Clustering

Our system displays the CPU and Price clustering to the company. The latter can
now select the most interested clustering for each attribute. These selections are
performed through the GUI of Figure 11. With these selections, our system knows the
range and depth of the company’s needs. The selection information, representing the
company’s purchasing interests, will help the system to create the interest model in
the following phases.
An Interest-based Offer Evaluation System for
Semantic Matchmakers 7

4 Inter est Weight Computation
An interest weight denotes the degree of importance of an attribute in a matching. It is
related to the depth and range of the buyer’s selection. The smallest and deepest
clustering shows the best interest of the buyer. In order to produce the attribute’s
weight, we use the interest-weight coefficient which contains the buyer’s interest in
one attribute. We create Formula (1) to compute the interest-weight coefficient of an
attribute called k: K is the attribute set, DataAttribute is the data range of k,
SelectedClustering is the buyer’s selected clustering for k, SelectedLevelTree is the
selected clustering level, and TotalLevelTree is the number of levels of the clustering
tree.
DataAttributek SelectedLevelTreek (1)
IW _ coek = ⋅ k∈K
SelectedClustering k TotalLevelTreek

This coefficient has to be compared with the other attributes’ coefficients (cf.
Formula (2)). An attribute with a larger interest weight coefficient gets a larger
interest weight compared to the other attributes. This means we can finally link the
interest weights with the buyer’s selection.
IW _ coek (2)
IWk = K
k∈K
∑ IW _ coe
k =1
k

Example. We suppose the company chooses the CPU clustering [2.2, 2.5]. This
selection is at level 2 of the 5-level CPU tree and all CPU data are in the range of [2.2,
(3.2, 3.2)]. So, the CPU interest weight coefficient is calculated as 4.4667 with
Formula (1). We perform the same calculation for the Price attribute with a
coefficient of 44.8125. According to Formula (2), we can get all the attribute interest
weights (as shown in Figure 13). For example, CPU interest weight is the following:
4.4667
IWCPU = ≈ 0.0906
4.4667 + 44.8125
Based on the interest weights of Figure 11, we can see that Price is much more
important than CPU. Such interest feature will help the company to select its best
supplier.

5 Inter est Rate Function Gener ation
The goal here is to produce the interest rate of each attribute. To do this, we need to
define an interest rate function. A linear function is usually used to measure the
attributes’ rates [1, 13, 14]. In some cases, linear utility functions cannot assign
weights to attributes in order to make an offer as the best one.
In order to solve this problem, we use the un-linear sigmoid function
1
ς k (x) =
1 + exp - x . We believe the sigmoid function is the closest function to human
natural interest change. In our work, we use the sigmoid function to simulate each
8 Samira Sadaoui and Wei Jiang

attribute interest rate. Here x denotes the value of an attribute and y its interest value.
In Figure 7, we show that x of the sigmoid function has less changes in the two
intervals [-∞, -2] and [2, +∞]. The Sigmoid function in these two intervals can be
considered as a linear function with an acceptable standard error. Meanwhile the
interval [-2, 2] is a quickly changeable area. A buyer’s selected attribute clustering
contains a specific interest for this attribute. In order to represent such interest in our
interest rate function, we need to bind the buyer’s selected clustering into the quickly
changeable interval [-2, 2].

Fig. 7. Sigmoid Function and Value Change

We employ LVQ as the center of our interest rate function since LVQ can be
considered as the density center of the attribute data. Attribute data [AL, AR] can be
distributed accordingly, and the selected clustering [L, R] is bound into the interval [-
2, 2]. LVQ point can be either in the selected clustering [L, R] or outside. If LVQ is
inside the clustering, we divide the interest rate function into two functions (cf.
Formula (3)) where αL is generated when binding [L, LVQ] into the interval [-2, 0], αR
is generated when binding [LVQ, R] into [2, 0], Sign is +1 or -1 w.r.t ascending or
descending order of the clustering.

 1
1 + exp [α L ⋅Sign⋅( x − LVQ)] x ∈ [ AL, LVQ]

ς Attribute (x) =
 1
x ∈ [LVQ, AR]

1 + exp
[α R ⋅ Sign⋅( x − LVQ )]

(3)

αL · (x - LVQ) = - 1· (-2 - 0) when x = L
αL = 2 / (L - LVQ)
αR . (x - LVQ) = - 1· (2 - 0) when x = R
αR = - 2 / (R - LVQ)
Example. We can now generate the interest rate functions for CPU and Price by
binding all the selected clustering into the quickly changeable area. According to the
CPU attribute tree, the selection [2.2, 2.5] has the LVQ of (2.4767, 0.3189). To be
An Interest-based Offer Evaluation System for
Semantic Matchmakers 9
able to process high dimensional values, we use the distance between attribute data
and the best attribute data. Here, we believe CPU clustering [3.2, 3.2] is the best data.

Table 2. Distances of High-Dimensional Data

x BestAttr ibuteData Distance(x, BestAttr ibuteData)
2.2 *** (3.2, 3.2) 3.3526
2.5 ** (3.2, 3.2) 3.2757
(2.4767,0.3189) * (3.2, 3.2) 2.9705
***Min selection value (L), **Max selection value (R), *LVQ

According to the distance calculation in Table 2, LVQ is closer to the best data than
any other data in the selected clustering. Consequently, the following interest rate
function for attribute CPU has only αL. Figure 8 displays the company’s interest rate
for CPU.

1
ς CPU (x) = [α L ⋅Sign⋅( Dis tan ce ( x , BestAttributeData ) − Dis tan ce ( LVQ,BestAttributeData) )]
1+ exp
1
= 5.2342⋅[ Dis tan ce ( x ,( 3.2 , 3.2 )) − 2.9705 ]
x ∈ [2.2, (3.2, 3.2) ]
1+ exp

Fig. 8. Interest Rate Function for CPU

We suppose that the company selected the Price clustering [(420, 400), (470, 370)]
which is then bound into the area [-2, 2]. After data binding, we produce the
following interest rate function where LVQ of 39.88 is its center point. Based on this
function, we can easily get the company’s interest rate for Price attribute as shown in
Figure 9.
10 Samira Sadaoui and Wei Jiang

 1
1 + exp [α L ⋅Sign⋅( Dis tan ce ( x , BestAttributeData ) − Dis tan ce ( LVQ,BestAttributeData) )]

ς Pr ice (x) =
 1
1 + exp [α R ⋅Sign⋅( Dis tan ce ( x , BestAttributeData ) − Dis tan ce ( LVQ,BestAttributeData) )]

 1
1 + exp 0.0502⋅[ Dis tan ce ( x ,( 420, 400 ))−39.88]

= x ∈ [(420,400), (3200, 2500)]
 1
1 + exp 0.1085⋅[ Dis tan ce ( x ,( 420, 400 ))−39.88]

Fig. 9. Interest Rate Function for Price

6 Offer Evaluation with the Inter est Model
MNL model expresses the utility for a group of people choosing an item. The utility
function for an individual in a population includes the deterministic and random
components as follows [13]:
K (4)
U nj = ∑ bk ⋅ x njk + ε nj , j ∈C
k =1

where
• Unj is the utility for buyer n selecting item j.
• (bk . xnjk) is the “representative” taste of the population. This component
consists of K observed deterministic features xnjk; bk is the weight for each
feature xnjk.
• εnj is the individual taste for the item j.
• C is the set of items.
An Interest-based Offer Evaluation System for
Semantic Matchmakers 11
We now adapt the MNL formula to define the interest model for each individual.
First we consider the deterministic features as the offer attributes. Furthermore, εnj can
be decomposed into K attributes since εnj is the total alternative with K features. This
means the evaluation of the attributes may be different according to the interests of
each individual (cf. Formula (5)). At the market level, the individual taste ε is brought
into the population model as a random utility. Since it comes from individuals and its
value is random, ε is usually removed when users generate the MNL model. However,
in our system individual taste becomes important.
K (5)
U nj = ∑ bk ⋅ ( xnjk + ε njk ) j ∈C
k =1

In order to produce the interest model, our system calculates the interest weights,
IW, and simulates the interest rates, IR. IW denotes the interest weight bk and IR the
interest rate value xnjk + εnj . We propose Formula (6) to build the interest model IM for
each individual. IM is the degree of interest of the buyer purchasing an offer j with K
attributes. Based on the consumer theory, an individual seeks to maximize his utility
in each purchasing behavior.
K
(6)
IM ( j ) = ∑ ( IW jk ⋅ IR jk ) j ∈C
k =1

Example. After our system gets the interest weights and interest rate functions for the
two attributes, it generates the interest model (IM) for the company as follows.

IMCompany(Supplier) = 0.0906·ϛCPU(CPU) + 0.9094·ϛPrice(Price)

For example, we show below how the interest model calculates the interests for
the first two suppliers:
IMCompany(Supplier1) = 0.0906 · ϛCPU(2.5) + 0.9094·ϛPrice[(1100,799)]
= 0.0906 · 0.1684 + 0.9094 · 6.2583E-36
= 0.0189
IMCompany(Supplier2) = 0.0906 · ϛCPU(2.2) + 0.9094 · ϛPrice[(470,370)]
= 0.0906·0.1192 + 0.9094·0.1192
= 0.1192
So, we got an interest rate of 0.0189 for Supplier1’s offer and 0.1192 for Supplier2’s
offer. Based on these values, we can conclude that Supplier2 has a higher chance than
Supplier1 to be the company’s partner. With our interest model, we can evaluate all
the candidate offers of Table 1. Table 3 shows that Supplier9 is the best supplier for
the company.
12 Samira Sadaoui and Wei Jiang

Table 3. Sorted Offers for the Company

Supplier ID CPU (GHz) Pr iceRange1 Pr iceRange2 Inter est
9* 2.2 420 400 0.8118
2 2.2 470 370 0.1192
12 (1.9, 1.9) * 800 700 0.0152
13 (3.0, 3.0) * 2900 2600 0.0127
14 (1.8, 1.8)* 680 680 0.0137
15 (3.2, 3.2)* 3200 2500 0.0152
11 2.8 1200 1150 0.0173
7 2.66 2500 2200 0.0122
3 2.5 600 500 0.0152
6 2.5 1030 830 0.0137
1 2.5 1100 799 0.0189
5 2.4 999 799 0.0903
10 2.4 950 900 0.0906
4 2.33 1100 800 0.0900
8 2.3 1000 880 0.0906
*: best offer with the max interest degree

7 Design and Implementation
We developed our system with a distributed architecture as illustrated in Figure 10.
The buyer interacts with the client side via the GUI. After the buyer submits his
request, the GUI passes it to the connected semantic matchmaker. On the server side,
the OfferManager component: (1) collects the request-matched offers and stores them
in the ContentOffer database, (2) analyzes the request and offers to extract the
attributes and their values, and (3) stores them in the OfferAttribute database. For each
attribute, the AttributeDataClustering component clusters its values and sends its
clustering tree to the client side. GUI helps the buyer to select the most interested
clustering (cf. Figure 11). Once the buyer’s selection is completed, the
InterestModelCreator component is called to build the buyer’s interest model. It first
passes all the selected clustering and the whole attribute clustering trees to
InterestWeightCalculator and InterestRateFunctionCreator . For each attribute,
InterestWeightCalculator returns the interest weight coefficient and interest weight
(cf. Figure 12), and InterestRateFunctionCreator the interest rate function. The
OfferEvaluator component applies the generated interest model on the candidate
offers, and returns to the buyer the list of offers sorted by interests. In Figure 13, we
show for instance the offer evaluation process on the client side.
An Interest-based Offer Evaluation System for
Semantic Matchmakers 13

Fig. 10. System Top-Level Architecture

Fig. 11. Selecting CPU and Price clustering
14 Samira Sadaoui and Wei Jiang

Fig. 12. Calculating the Interest Weights for CPU and Price

We implemented our system using Visual Studio C# (on the .net 3.5 framework)
and the SQL Server 2008. Figures 14 and 15 show the class diagrams of the client and
server side programs. We created two separate databases sources, called serverDB
and clientDB, to support server and client side programs. These two classes contain
all the necessary functions about database processing and data binding. The other
classes are created with a window interface by using software MS Blend3, the
interface developing tool for Windows form application. The classes ServerWindow
and ClientWindow contain multi-thread and network communication functions.

Fig. 13. Client Side: Offer Evaluation
An Interest-based Offer Evaluation System for
Semantic Matchmakers 15

Fig. 14. Server Side Class Diagram

Fig. 15. Client Side Class Diagram

8 CONCLUSION AND FUTURE WORK
In this paper, we showed the benefits of sorting the request-matched offers according
to the buyer’s interests and needs. Our interest model provides a solution to existing
matchmaking systems and avoids the linear matching problems. Adopting an
economic method, we produced a simple and automated model to determine the best
matched offer based on the buyer’s selections.
One possible direction of this work is to include the interest learning [19] in our
system. The main purpose of this learning is to update the interest model to fit the
buyer’s interests instantly. A learned interest model will be able to determine the best
offer in these two situations: the buyer shifts his interests, or new offers are added in
our database.
16 Samira Sadaoui and Wei Jiang

Refer ences
1. Hahn, C., Nesbigall, S., Warwas, S., Zinnikus, I., Klusch, M., and Fischer, K. Model-
Driven Approach to the Integration of Multi-Agent Systems and Semantic Web Services.
Enterprise Distributed Object Computing Conference Workshops. IEEE. 314-324 (Sept.
2008)
2. Wang, H., and Li. Z. Z.: A Semantic Matchmaking Method of Web Services Based on
SHOIN^+ (D)*. Services Computing. IEEE. 26-33 (Dec. 2006)
3. Kawamura, T., Hasegawa, T., Ohsuga, A., Paolucci, M., and Sycara, K.: Web services
lookup: a matchmaker experiment. IT Professional. IEEE. Vol. 7, No. 2, 36-41 (Mar-Apr
2005)
4. Qiu, T., and Li, P. F.: Web Service Discovery Based on Semantic Matchmaking with
UDDI. In Proceedings of the The 9th International Conference. Young Computer
Scientists. 1229-1234 (Nov. 2008)
5. Bai, D. W., Liu, C. C., Peng, Y. and Chen, J. L.: Web Services Matchmaking with
Incremental Semantic Precision. Wireless Communications, Networking and Mobile
Computing. IEEE. 1-4 (Sept. 2006)
6. Bai, D., Fei, A. G., and Cai, S. F.: Semantic Matchmaking of Web Services Constraint
Conditions. Wireless Communications, Networking and Mobile Computing. IEEE. 1-5
(Sept.2009)
7. Huang, R., Zhuang, Y. W., Zhou, J. L., and Cao, Q. Y.: Semantic Web-based Context-
aware Service Selection in Task-computing. In Proceedings of the WMSO '08
International Workshop. 97-101 (Dec. 2008)
8. Bellur, U., and Vadodaria, H.: Web Service Ranking Using Semantic Profile Information.
In Proceedings of the ICWS 2009. IEEE. 872-879 (July 2009)
9. Essex, D.: Matchmaker, matchmaker. ACM Communications, ACM. Vol. 52, 16-17 (May
2009),
10. Kohonen, T.: The Self-Organizing Map, 3rd Edition. Springer (2001)
11. Chen, Y. Y., and Young. K. Y.: Applying SOM as a Search Mechanism for Dynamic
System. Decision and Control, IEEE. 4111- 4116 (Dec. 2005)
12. McFadden, D.: Economic Choices. American Economic Association. American Economic
Review. Vol. 91, No. 3, 351-378 (Jun. 2001)
13. McFadden, D., and Zarembka, P.: Conditional logit analysis of qualitative choice
behavior. Academic Press. Frontiers in Econometrics. 105-142 (1974)
14. Ha, S. H., and Park, S. C.: Matching buyers and suppliers: an intelligent dynamic
exchange model. IEEE. Intelligent Systems. Vol. 16, No. 4, 28- 40 (Jul-Aug 2001)
15. Wang, X., Vitvar, T., Kerrigan, M., and Toma, I.: A QoS-aware selection model for
semantic web services. In Proceedings of the 4th Int. Conference on Service-Oriented
Computing. 390–401 (2006)
16. Yu, Q., and Reiff-Marganiec, S.: Non-functional property based service selection: a
survey and classification of approaches. In Proceedings of the Non Functional Properties
and Service Level Agreements in SOC Workshop (Nov. 2008)
17. QoS for Web Services: Requirement and Possible Approaches. http://www.w3c.or.kr/kr-
office/TR/2003/ws-qos/
18. Web Services Description Language (WSDL) 1.1, http://www.w3.org/TR/wsdl
19. Wei, Y.Z., Moreau, L., Jennings, N.R.: Learning users' interests by quality classification in
market-based recommender systems. Knowledge and Data Engineering. IEEE
Transactions. Vol.17, No. 12, 1678- 1688 (Dec. 2005)