CAJ | 학술논문

信息大爆炸的网络时代，个性化推荐是解决信息“超负载”的有效办法。用户兴趣模型是个性化推荐的核心，关系着整个推荐系统的推荐质量。标签一直被用于资源分类，在个性化推荐方面却很少使用。文中采取向量空间模型的建模方法，利用个性化标签描述用户兴趣，并提出一套简洁有效的标签标准化方法-基于属性共现率的标签标准化以及基于聚类的标签标准化方法对用户的自定义标签进行标准化。该模型能有效降低用户兴趣模型的向量维数，避免分析标签语义的复杂过程，且能够从用户的角度贴切地表达用户兴趣。实验结果表明该模型有助于提高个性化推荐的服务质量。
신식대폭작적망락시대，개성화추천시해결신식“초부재”적유효판법。용호흥취모형시개성화추천적핵심，관계착정개추천계통적추천질량。표첨일직피용우자원분류，재개성화추천방면각흔소사용。문중채취향량공간모형적건모방법，이용개성화표첨묘술용호흥취，병제출일투간길유효적표첨표준화방법-기우속성공현솔적표첨표준화이급기우취류적표첨표준화방법대용호적자정의표첨진행표준화。해모형능유효강저용호흥취모형적향량유수，피면분석표첨어의적복잡과정，차능구종용호적각도첩절지표체용호흥취。실험결과표명해모형유조우제고개성화추천적복무질량。
Faced to the Internet age of information explosion,the personalized recommendation is an effective way to solve the“informa-tion overload”. User interest model as the core of personalized recommendation determines the quality of the recommendation system. Tags have been used for the classification of resources;however,they are seldom used in personalized recommendation. In this paper,vec-tor space model is used in modeling,where personalized tags are used to describe user interests. A set of simple and effective methods are proposed to standardize user's custom tags,including a standardization method based on attribute co-occurrence frequency and a stand-ardization method based on clustering. Thus,the vector dimension of the user interest model can be reduced effectively,avoiding complex tag semantic analysis,as well as being able to aptly express user's interests from their point of view. The experimental results show that the proposed user interest model can help to improve the quality of personalized recommendation.