CAJ | 학술논문

讨论基于Mahout的推荐系统开发过程，以我校学生对学校食堂各就餐窗口进行评分为例，通过建立实验系统，利用Mahout推荐系统引擎提供的API分析实验数据。该实验样例完整地阐述创建自己的推荐引擎构造器的过程。本实验采用常用的协同过滤推荐算法，协同过滤推荐算法主要包括基于用户的协同过滤，基于物品的协同过滤以及Slope-One推荐算法。搭建自己的开发环境，基于相似度算法模型和推荐算法，使用7种不同组合进行对比实验。使用查准率和召回率两个指标对7种算法组合进行评估。采用欧氏距离用户相似度，基于用户和物品的推荐算法，并且采用有评分和无评分的方法对推荐结果进行比较，由实验得知，基于Mahout的推荐系统能快速高效地给学生推荐相似的就餐窗口。
토론기우Mahout적추천계통개발과정，이아교학생대학교식당각취찬창구진행평분위례，통과건립실험계통，이용Mahout추천계통인경제공적API분석실험수거。해실험양례완정지천술창건자기적추천인경구조기적과정。본실험채용상용적협동과려추천산법，협동과려추천산법주요포괄기우용호적협동과려，기우물품적협동과려이급Slope-One추천산법。탑건자기적개발배경，기우상사도산법모형화추천산법，사용7충불동조합진행대비실험。사용사준솔화소회솔량개지표대7충산법조합진행평고。채용구씨거리용호상사도，기우용호화물품적추천산법，병차채용유평분화무평분적방법대추천결과진행비교，유실험득지，기우Mahout적추천계통능쾌속고효지급학생추천상사적취찬창구。
Constructs an experimental system based on the deep discussion on development process of Mahout recommended system and empirical researches on scoring all dining windows by students. Furthermore, conducts a deep analysis on experimental data through API provided by Mahout recommended system engine. This experimental sample completely narrates the process to create its own recommended engine constructor. The commonly-used collaborative filtering recommendation algorithm is adopted by this experiment which includes collabora-tive filtering of users, collaborative filtering of articles and recommendation algorithm of SlopeOne. Under construction of its own develop-ment environment, adopts seven different combinations to conduct contrast experiment based on similarity algorithm model and recom-mended algorithm. Precision ratio and recall rate are applied to evaluate the seven algorithm combinations. Compares these recommended results through Euclidean distance similarity of users, collaborative filtering of users, collaborative filtering of articles, scoring method and non-scoring method. From the experimental results, it can be concluded that recommended system of Mahout can efficiently and rapidly recommend similar dining windows to students.