CAJ | 학술논문

面向基于情境感知的推荐问题，提出一种基于用户情境聚类的个性化推荐算法。该算法利用情境预过滤的思想，首先运用模糊聚类的方法对历史数据集中用户的情境进行聚类，构造与当前用户情境相似度较高的用户集合，再与传统的基于用户的协同过滤算法相结合进行个性化推荐。实验采用公开数据集，结果表明该算法在多维情境信息条件下可用，并且推荐准确度要高于传统协同过滤算法，在聚类粒度不同的情况下对推荐结果也会产生不同的影响。
면향기우정경감지적추천문제，제출일충기우용호정경취류적개성화추천산법。해산법이용정경예과려적사상，수선운용모호취류적방법대역사수거집중용호적정경진행취류，구조여당전용호정경상사도교고적용호집합，재여전통적기우용호적협동과려산법상결합진행개성화추천。실험채용공개수거집，결과표명해산법재다유정경신식조건하가용，병차추천준학도요고우전통협동과려산법，재취류립도불동적정황하대추천결과야회산생불동적영향。
Towards the problem of recommendation based on context-aware,propose a personalized recommendation algorithm based on user context clustering. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users' context in history data set first to construct the user set which is similar with current user context,and then combined with user-based collabora-tive filtering algorithm for personalized recommendation. The proposed methodology is tested using public datasets and the results show that it can be used in multidimensional context information. The recommendation precision is higher than traditional collaborative filtering algorithm. And find the change of the cluster size has an impact on the recommendation result.