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Non-hierarchical Clustering based Hybrid Recommendation using Context Knowledge  

Baek, Ji-Won (Department of Computer Science, Kyonggi University)
Kim, Min-Jeong (Department of Computer Science, Kyonggi University)
Park, Roy C. (Department of Information Communication Engineering, Sangji University)
Jung, Hoill (Department of Computer Software, Daelim University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.20, no.3, 2019 , pp. 138-144 More about this Journal
Abstract
In a modern society, people are concerned seriously about their travel destinations depending on time, economic problem. In this paper, we propose an non-hierarchical clustering based hybrid recommendation using context knowledge. The proposed method is personalized way of recommended knowledge about preferred travel places according to the user's location, place, and weather. Based on 14 attributes from the data collected through the survey, users with similar characteristics are grouped using a non-hierarchical clustering based hybrid recommendation. This makes more accurate recommendation by weighting implicit and explicit data. The users can be recommended a preferred travel destination without spending unnecessary time. The performance evaluation uses accuracy, recall, F-measure. The evaluation result was shown 0.636 accuracy, 0.723 recall, and 0.676 F-measure.
Keywords
Context Information; Clustering; Recommendation System; Knowledge Recommendation;
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