The prototype system CAR uses the rule-based knowledge base and the case-based knowledge base concurrently. After the hybrid knowledge base is Table 2. Example of Association Rules from Web Log Database
Pocket Booster (Checker) <= RC car guidebook (5:4.673%, 0.2) ACE 2000 <= 7.2V low speed charger (5:4.673%, 0.4)
Table 3. CBL Algorithm
Similarity = Customer’s characteristics + probability of success
Probability of success = constant
purchase
Customer’s characteristics =
∑
=ni
built, CAR can execute inference. In this phase, CAR may suggest the results of hybrid recommendation to the customer and then wait for the customer’s feedback and response. Before the inference, Table 4 shows the web customer’s brief profile and preferences to validate our hybrid recommendation mecha-nism.
First, the customer will search and select the guidebook for a remote-controlled car. If this web site is a common shopping mall, however, he can’t get additional information about the ability to control the remote- controlled car.
Therefore, the web site may lose this potentially loyal customer. In this case, CAR can present more intelligent and additional information to customers.
Figure 6 shows the hybrid recommendation results of CAR.
In Figure 6, the customer finds additional information describing other products suggested by CAR. Finally, the recommended products (information) are ‘SuperNova 3000S (re-charger for a worn out battery),’ ‘Switching Power 15A (high capacity power supplier),’ and ‘3-Mode Charger (re-charger for remote controller, receiver and battery).’ These products are the most impor-tant and basic goods for controlling the remote-controlled plastic models. As a result, the customer may purchase the product he wants and, at the same time, find additional products.
Figure 5. Case-Based Knowledge Base
(*Experience: months experienced, Interest: 1=car/tank, 2=yacht/ship, 3=airplane/
helicopter)
Table 4. Customer’s Profile and Preference
C ustom er ’s profile
Birth: February 1963 / Sex: M ale / Position: Businessm an / Experience (career): 7 m onths /
Interest: Car (rem ote controlled car)
C ustom er ’s preference:
Purchasing the G uidebook for rem ote-controlled car
CONCLUSION
This chapter suggests a hybrid recommendation mechanism and a proto-type system called CAR. The hybrid recommendation mechanism is based on association rule mining and on CBR, which is aimed at enriching the recom-mended information.
The proposed mechanism consists of a four phase-association rule gen-eration, case gengen-eration, construction of a hybrid knowledge base, and hybrid recommendation. The result of our experiment with an illustrative web log database proved to be valid and robust.
In conclusion, this study shows how the tacit knowledge within a web site can be brought together to create valuable decision support tools for an Internet Business focused on B2C (Business to Consumer). It is expected that the proposed recommendation mechanism will have a significant impact on the research domain related to B2C Internet Business and CRM. Further research topics still remaining are as follows:
(1) The basic technology of data mining used for this study needs to be improved so that more complicated customer knowledge can be ana-lyzed.
Figure 6. Hybrid Recommendation Results of CAR
Consistent recom m endation by Rule-based reasoning and CBR:
Booster ¡ æChecker for battery
Consistent recom m endation by Rule-based reasoning and CBR:
Booster ¡ æChecker for battery
Additional recom m endation by CBR:
SuperNova 3000S a¡ æRe-charger for out-w arned battery Switching Pow er 15A ¡ æHigh capacity power supplier 3-M ode charger ¡ æRe-charger for controller, receiver & battery Additional recom m endation by CBR:
SuperNova 3000S a¡ æRe-charger for out-w arned battery Switching Pow er 15A ¡ æHigh capacity power supplier 3-M ode charger ¡ æRe-charger for controller, receiver & battery Hybrid recom m endation:
1. Association rule-based reasoning 2. Case-based reasoning Hybrid recom m endation:
1. Association rule-based reasoning 2. Case-based reasoning
(2) CBR needs to be integrated with other artificial intelligence-based rea-soning algorithms, such as fuzzy cognitive map (FCM), so that more complicated web-based decision problems can be analyzed effectively.
(3) CAR, our prototype system, needs to be updated with other commercial functions so that more practical recommendation problems can be solved easily.
ACKNOWLEDGMENTS
This work was supported by a Korea Research Foundation Grant (KRF-2002-003-B00099).
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