CAJ | 학술논문

信息推荐系统主要根据已有的用户历史信息来对未知信息进行预测。但用户的活跃度往往使得数据集本身过于稀疏，从而使相关算法产生过拟合问题。跨域推荐算法是为了解决在单域推荐中常遇到的数据稀疏性问题，然而大多数的推荐算法在考虑共享信息时并未考虑单个数据域的个性信息。本文通过矩阵聚类方法来提取矩阵的潜在因式，区别数据集合之间的共享信息和自身信息。通过这种方法来做跨域推荐预测，并在几个现实中的数据集上与现有的一些推荐算法进行比较。
신식추천계통주요근거이유적용호역사신식래대미지신식진행예측。단용호적활약도왕왕사득수거집본신과우희소，종이사상관산법산생과의합문제。과역추천산법시위료해결재단역추천중상우도적수거희소성문제，연이대다수적추천산법재고필공향신식시병미고필단개수거역적개성신식。본문통과구진취류방법래제취구진적잠재인식，구별수거집합지간적공향신식화자신신식。통과저충방법래주과역추천예측，병재궤개현실중적수거집상여현유적일사추천산법진행비교。
Recommender system aims to predict the unknown information based on the existing user history information. However, the inacitivity of users leading the data set too sparse and causing the overift problem of certain algorithms.Cross-domain recommendations are proposed to solve the problem of sparsity.While most of them consider the sharing message between domains,they ignore the individual information of each data set. In this paper, we ifnd the latent factor model by clustering the matrix and distinguish the common information and the different.In this paper,we compare our model with several current recommender system on several real data sets.