CAJ | 학술논문

协同过滤算法中，相似性计算方法是整个推荐系统的关键，决定着推荐系统的推荐质量，为了提高相似性计算的精准性，提出了一种基于时间衰减的相似性计算方法。该方法在计算用户相似性时，考虑目标物品与共同评分物品的相似性，同时在计算用户与物品相似性时，考虑时间信息(用户对物品产生行为的时间)对相似性的影响。实验结果表明，该方法能够有效地避免传统相似性计算方法的不足，使推荐系统获得更好的推荐效果。
협동과려산법중，상사성계산방법시정개추천계통적관건，결정착추천계통적추천질량，위료제고상사성계산적정준성，제출료일충기우시간쇠감적상사성계산방법。해방법재계산용호상사성시，고필목표물품여공동평분물품적상사성，동시재계산용호여물품상사성시，고필시간신식(용호대물품산생행위적시간)대상사성적영향。실험결과표명，해방법능구유효지피면전통상사성계산방법적불족，사추천계통획득경호적추천효과。
In collaborative filtering algorithm, similarity calculation method is undoubtedly the key of the entire recommender system, and determines the recommended quality of recommender system. In order to improve the precision of similarity calculation, a new similarity calculation method based on time decay is proposed. In this method, the similarity between the objective item and common-scored items is taken into account when calculating users similarity, and the influence of time information is taken into account when calculating users and items similarity. Experimental results shows that this method can effectively avoid the lack of the traditional similarity calculation method, and make the recommender system gain better recommendation result.