발간년도 : [2019]
| 논문정보 |
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| 논문명(한글) |
[Vol.14, No.4] A Recommendation Technique Based on Offline Product Using Similarity |
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| 논문투고자 |
Chul-Jin Kim, Cheon-Woo Jo, Ji-Hyun Jeong |
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| 논문내용 |
The online shopping mall can efficiently provide the recommendation product to the user by using the purchase transaction information of the user. However, in the offline store, there is a limit in providing recommended products in real time using information of users or purchase transaction information. Currently, O2O service provision is spreading, but development and research on personalized recommendation service based on offline products are insufficient. In this paper, we propose an architecture for recommending products suitable for users by calculating similarity between products based on offline individual products and online transaction information. We also propose a procedure for deriving a recommendation product among the constituent modules constituting the architecture. The offline individual product is identified through the Beacon sensor, and the user selects the offline product received from the beacon sensor to determine interest. It calculates the similarity based on offline products and online transaction information and provides top-n recommended products to users. We prove the feasibility of the architecture of this study by constructing a system that recommends products that interest the user by calculating the similarity for offline clothing of clothing store. The existing researches recommend brand based on the purchase history of the offline store visited by the user, but in this paper, it is different in terms of providing recommended products for individual products. |
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