CAJ | 학술논문

在分析现有网络商品推荐算法的优缺点及主要问题的基础上，提出一种基于混合模式的网络超市商品推荐方法。其主要思想是：通过商品本体概念和属性构建商品子模型，采用基于内容的推荐算法填充用户——商品评分矩阵；依据用户背景信息、评分数据和查询关键字构建用户子模型，采用K均值算法进行用户聚类；利用基于用户的协同过滤产生推荐。实验表明，混合算法提供的推荐结果更加准确高效。
재분석현유망락상품추천산법적우결점급주요문제적기출상，제출일충기우혼합모식적망락초시상품추천방법。기주요사상시：통과상품본체개념화속성구건상품자모형，채용기우내용적추천산법전충용호——상품평분구진；의거용호배경신식、평분수거화사순관건자구건용호자모형，채용K균치산법진행용호취류；이용기우용호적협동과려산생추천。실험표명，혼합산법제공적추천결과경가준학고효。
Aftex analyzlng the advantages and disadvantages of the current recommendation algorithms and the main prob- lems of algorithms, the online supermarket goods recommendation method based on mixed mode had been put forward. In order to fill the user- rating- data matrix via adopting contem- based recommendation, this paper constructed commodity sub- model by using good ontology concepts and attributes of goods. In the same way, it constructed user sub - model by using lmelound infor- marion, rating data and query keywords of user to complete the k- means clustering. Then the results were generated by means of collaborative fiiterin~ algorithm. The experimental results suggested that hybrid algorithm revealed good performance.