CAJ | 학술논문

使用协同过滤进行推荐，在处理大数据集时存在效率问题和推荐结果质量不高的问题。 k均值聚类在处理大数据集时有着较好的性能。针对使用协同过滤进行推荐存在的问题，通过使用遗传算法将聚类和协同过滤组合起来进行项目推荐，以此来提高推荐算法的推荐效率和推荐质量，降低组合聚类和协同过滤进行推荐的复杂度。使用组合得到的算法在MovieLens数据集上做推荐对比实验，结果表明，相比单纯使用协同过滤进行推荐，使用基于遗传算法的聚类与协同过滤组合推荐算法进行项目推荐，能得到质量更好的推荐结果。
사용협동과려진행추천，재처리대수거집시존재효솔문제화추천결과질량불고적문제。 k균치취류재처리대수거집시유착교호적성능。침대사용협동과려진행추천존재적문제，통과사용유전산법장취류화협동과려조합기래진행항목추천，이차래제고추천산법적추천효솔화추천질량，강저조합취류화협동과려진행추천적복잡도。사용조합득도적산법재MovieLens수거집상주추천대비실험，결과표명，상비단순사용협동과려진행추천，사용기우유전산법적취류여협동과려조합추천산법진행항목추천，능득도질량경호적추천결과。
When dealing with item recommendation with large data sets,there are problems of efficiency and the low quality of the results for collaborative filtering. K-means clustering has a better performance when processing large data sets. In order to solve problems of col-laborative filtering,genetic algorithm can be used to combine clustering and collaborative filtering for item recommendation to improve the efficiency and quality of the recommendation algorithm,reduce the complexity of item recommendation by the combination of cluste-ring and collaborative filtering. Do comparative experiments using the combination algorithm in Movielens data sets. The experimental re-sults show that,compared with pure collaborative filtering recommendation,using genetic algorithm to combine clustering with collabora-tive filtering for item recommendation can get a better quality results.