An Offer Evaluation System based on Buyers’ Interests Samira Sadaoui
Wei Jiang
University of Regina 3737 Wascana Parkway Regina, SK, Canada, S4S 0A2
University of Regina 3737 Wascana Parkway Regina, SK, Canada, S4S 0A2
[email protected]
[email protected]
ABSTRACT
Matchmaking systems failed to provide the best matched offer to each individual. In this paper, we develop an interest-based offer evaluation system for semantic matchmakers. Our system returns the best offer by sorting the request-matched offers according to the buyer’s favors and interests. The best offer represents the maximum satisfaction of the buyer. Our system captures and analyzes individual interests to bring better results for each buyer as demonstrated in our case study.
according to the buyer’s favors and interests. To better illustrate the benefits of our system, we consider the case where there are several buyers looking for the same service. In Section 3, we demonstrate how our system recommends different best offer for each buyer. We modify the MultiNomial Logit (MNL) model [5] to provide an interest model that evaluates the offers w.r.t buyer’s interests. We employ the clustering technology Self Organizing Map (SOM) [4] to take into account the buyer’s interests and also to be able to cluster high-dimensional attribute data.
Categories and Subject Descriptors
2. OFFER EVALUATION PROCESS
H.3.5 [Information Storage And Retrieval]: Information Services – Web-based services.
The buyer submits a purchasing request which is sent to the connected matchmaker. The latter returns a list of requestmatched offers evaluated in the following phases.
On-line
General Terms
A. Clustering Attribute Data. Our system determines all the attributes from the buyer’s request and extracts their values from the candidate offers. Based on SOM [4], it clusters the values of each attribute, so that the buyer can select one of the clustering to represent his most interested area.
Experimentation, Human Factors.
Keywords
MultiNomial Logit model, clustering technology, semantic matchmaking, interest model.
B. Computing Attribute Interest Weights. The interest weight denotes the degree of importance of an attribute in a matching. We define the two formulas (1) and (2) to compute the interestweight coefficient and interest weight of an attribute k: K is the attribute set, DataAttribute the data range of k, SelectedClustering the buyer’s selected clustering for k, SelectedLevelTree the level of the selected clustering, and TotalLevelTree the number of levels of the clustering tree.
1. INTRODUCTION
Nowadays with the blooming of web services, users can obtain more and more request-matched services (offers) through semantic matchmaking [2, 6, 1, 3]. It is time consuming for users to evaluate all the candidate offers in order to find the best one. Today finding the best offer is more important than ever before for any matchmaker. The best offer denotes the highest semantic matching degree. Nevertheless, matchmakers do no guarantee that the best offer will be purchased by the buyer. The buyer’s choice can be caused by other criteria referred to as non-functional properties [8]. To address this issue, matching methods have been introduced to evaluate the non-functional properties of services [8] with QoS requests [7]. Matchmakers failed to recognize the differences between buyers’ interests. Matchmaking based on semantic can help the buyer find the requested offers but it is not good enough to find the best offer. Consequently, we develop an interest-based offer evaluation system for semantic matchmakers. Our system returns the best offer by sorting the candidate offers
IW _ coek =
IWk =
DataAttributek SelectedLevelTreek ⋅ SelectedClustering k TotalLevelTreek
IW _ coek
k∈K
K
∑ IW _ coe k =1
k∈K
(1) (2)
k
C. Computing Attribute Interest Rates. A linear function is usually used to measure the attribute rates [5, 8]. In some cases, linear functions cannot assign weights to attributes to make an offer as the best one. To solve this problem, we use the un-linear 1 ς k (x) = 1 + exp - x to simulate an attribute’s sigmoid function
interest rate. This function has less changes in the two intervals [∞, -2] and [2, + ∞] and can be considered as a linear function. Meanwhile the interval [-2, 2] is the quickly changeable area. To represent the buyer’s interest for an attribute, we need to bind the selected clustering into [-2, 2].
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D. Sorting Offers with the Interest Model. The MNL model expresses the utility of a buyer n selecting item j [5]:
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K
U nj = ∑ bk ⋅ x njk + ε nj where xnjk is a deterministic feature, bk its k =1
weight and εnj the individual taste for the item j. To produce the interest model for each buyer, our system calculates the attributes’ interest weights, IW, and simulates their interest rates, IR. IW denotes the weight bk and IR the deterministic value (xnjk +εnj). We propose the model IM which returns the degree of interest of a buyer purchasing an offer j with K attributes: K
IM ( j ) = ∑ ( IW jk ⋅ IR jk )
The system builds the interest model for each buyer as follows. IMBuyer1(Offer)=0.1469·ϛCPU(CPU) + 0.1211· ϛRAM (RAM) + 0.0461·ϛHardDrive(HardDrive) + 0.6859·ϛPrice(Price) IMBuyer2(Offer)=0.0789·ϛCPU(CPU) + 0.0231·ϛRAM (RAM) + 0.0728·ϛHardDrive(HardDrive) + 0.8252·ϛPrice(Price)
(3)
k =1
In Table 2, the system evaluates all the candidate offers: Offer12 is the best offer for Buyer1 and Offer1 for Buyer2.
3. EXPERIMENTATION
We have two buyers, Buyer1 and Buyer2, who submit the same query: ``request a computer with CPU ＞ 1.5 GHz, RAM ＞ 2.0 GB, Hard Drive ＞ 100 GB, and Price ＜ $3500’’. We assume the matchmaker returns the candidate offers given in Table 1.
Offer ID
IM Buyer1
Offer ID
IM Buyer2
12
*0.9022 (Best)
1
*0.7295 (Best)
14
0.8780
2
0.6178
Table 1. Candidate Offers
10
0.7470
15
0.1654
3
0.7245
13
0.1483
2
0.7124
3
0.1388
1 …
0.7089
12
0.0982
…
…
…
Offer ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CPU (GHz) 2.2 2.2 2.5 2.33 2.4 2.5 2.66 2.3 2.5 2.4 2.8 (1.9, 1.9) * (3.0, 3.0) * (1.8, 1.8)* (3.2, 3.2)*
RAM (GB) 2 3 4 8 8 6 12 12 6 4 6 10 16 9 12
Hard Drive (GB) 320 640 500 1310 750 750 1024 960 820 1200 620 1000 1500 160 2000
Table 2. Sorted Offers for both Buyers
Price ($) 420 470 600 1100 999 1030 2500 1000 1100 950 1200 800 2900 6800 3200
4. CONCLUSION AND FUTURE WORK
We showed the benefits of sorting the request-matched offers according to buyers’ interests and favors. The future work is to define a learned interest model to be able to instantly determine the best offer in two situations: the buyer shifts his interests; new offers are added to our system.
5. REFERENCES
Our system builds the clustering trees for the four attributes (see Figure 1 for an example).
[1] Dong-wei, B., Ai-guo, F., Shan-fa, C. Semantic Matchmaking of Web Services Constraint Conditions. 5th Int. Conference on Wireless Communications, Networking and Mobile Computing, IEEE, (Sept.2009), 1-5. [2] Dong-wei, B., Chuan-Chang, L., Yong, P., Jun-liang, C. Web Services Matchmaking with Incremental Semantic Precision. Int. Conference on Wireless Communications, Networking and Mobile Computing, IEEE, (Sept. 2006), 1-4. [3] Huang, R., Zhuang, Y., Zhou, J., Cao, Q. Semantic WebBased Context-Aware Service Selection in Task-Computing. Int. Workshop on Modelling, Simulation and Optimization, IEEE, (Dec. 2008), 97-101.
Figure 1. Clustering CPU High-Dimensional Data The two buyers are requested to select their most interested attribute clustering. The system can now compute the attributes’ interest weights and generate their interest rate functions (see Figure 2 for an example).
[4] Kohonen, T. The Self-Organizing Map, 3rd Edition, Springer –Verlag New York Inc., 2001. [5] McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior. Frontiers in Econometrics, ed. P. Zarembka, Academic Press: New York, (1974), 105-142.
1 1 1+ exp ( 0.0236| x − 704 |) x ∈ [160, 704] 1 + exp ( 0.0162 | x − 1083 |) x ∈ [160, 1083] ς HardDrive _ Buyer 2 (x) = ς HardDrive _ Buyer1 (x) = 1 1 x ∈ [704, 2000] x ∈ [1083, 2000] ( -0.0174 | x − 704 |) ( −0.0171 | x − 1083 |) 1+ exp 1 + exp
[6] Qiu, T., and Li, P. Web Service Discovery Based on Semantic Matchmaking with UDDI. 9th Int. Conference for Young Computer Scientists, IEEE, (Nov. 2008), 1229-1234. [7] W3C. QoS for Web Services: Requirements and Possible Approaches. http://www.w3c.or.kr/kr-office/TR/2003/wsqos/. [8] Yu, H. Q., and Reiff-Marganiec, S. Non-Functional Property based Service Selection: A Survey and Classification of Approaches. Non Functional Properties and Service Level Agreements in Service Oriented Computing Workshop, ECOWS 2008, IEEE, (Nov. 2008).
Figure 2. Interest Rate Functions for Hard Drive
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