earticle

영어

In this paper, we present a novel method for improving contents recommendation accuracy using LBS-based users viewing path similarity. We have previously presented a user similarity-based contents recommendation algorithm using NFC. However, the existing research might not recommend contents related with exhibits which users did not tag but do like because it uses only the information that users tagged the exhibits. Also, it can decrease the quality of service (QoS) because it uses tagging patterns of users that were not interested in the exhibits but tagged them without care and thought. In this paper, to solve these problems of our existing service, we divide an exhibition into the areas through analyzing the wifi signal strength and analyze the areas where the user stays long by using LBS and measure the similarity based on the viewing path between users. By using this analyzed information, the proposed service can recommend contents related with exhibits which are the user`s favorite, but not tagged by the user. Also, it might prevent the degradation of QoS of the existing service because it uses the above mentioned information and the measured similarity.