• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.8 s.350
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• pp.15-24
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• 2006

We design and implement explicit mobile multicast, named XMIP, by enhancing explicit multicast for a great number of small group multicast communications. XMIP is a straightforward multicast mechanism without maintaining multicast states due to the inheritance from the explicit multicast based on a unicast routing. This research modifies and extends the functionality of each mobility agent of IETF Mobile IP for interworking XMIP XMIP Packets captured by an extended home agent are forwarded to each extended foreign agent through nested tunnels, named X-in-X tunnels, made by the binding table of the extended home agent. X-in-X tunneling mechanism can effectively solve the serious traffic concentration problems of Mobile IP multicast specifications. Finally heterogeneous mobile networks as an XMIP testbed including CDMA2000 1X EV-DO and WLAN are actually established, and a multi-user instant messenger system for small group communications is developed for verifying the feasibility of the proposed protocols.

• The KIPS Transactions:PartC
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• v.12C no.2 s.98
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• pp.267-280
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• 2005

In this paper, we implement and verify XMIP integrating IETF Mobile IP and the Explicit Multicast mechanism for a great number of small group multicast communications. U a source node sends Xcast packets explicitly inserting destination nodes into the headers, each Xcast router decides routes and forwards the packets toward each destination node based on unicast routing table without the support of multicast trees. n is a straightforward and simple multicast mechanism just based on a unicast routing table without maintaining multicast states because of the inheritance from the Explicit Multicast mechanism. This research modifies and extends the functionality of IETF Mobile IP's mobility agents, such as HA/FA to HA+/FA+ respectively, considering interworking with Xcast networks. Xcast packets captured by HA+ are forwarded into X-in-X tunnel interfaces for each FA+ referred to the binding table of HA.. This X-in-X tunneling mechanism can effectively solve the traffic concentration problem of IETF Mobile IP multicast services. Finally WLAN-based testbed is built and a multi-user Instant messenger system is developed as a Xcast application for finally verify the feasibility of the implemented XMIP/Xcast protocols.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.