CAJ | 학술논문

目前大多数个性化推荐算法为了追求较高的推荐精度而在不同程度上受到用户兴趣过拟合问题的影响，因此提出通过挖掘用户隐含的兴趣信息进行推荐的算法。首先利用概率主题模型抽取用户兴趣分布，并建立用户-兴趣-项目加权三部图。然后在用户-兴趣和兴趣-项目的概率加权二部子图上依次利用物质扩散算法配置项目资源值，并根据项目资源值的高低排序产生 Top-K 推荐。在 Movielens 数据集上的实验表明，基于用户-兴趣-项目三部图的推荐算法能缓解过拟合问题，同时可提高准确率等方面的性能。
목전대다수개성화추천산법위료추구교고적추천정도이재불동정도상수도용호흥취과의합문제적영향，인차제출통과알굴용호은함적흥취신식진행추천적산법。수선이용개솔주제모형추취용호흥취분포，병건립용호-흥취-항목가권삼부도。연후재용호-흥취화흥취-항목적개솔가권이부자도상의차이용물질확산산법배치항목자원치，병근거항목자원치적고저배서산생 Top-K 추천。재 Movielens 수거집상적실험표명，기우용호-흥취-항목삼부도적추천산법능완해과의합문제，동시가제고준학솔등방면적성능。
Since most of the existing personalized recommendation algorithms pursue a higher accuracy, their performance is affected by the problem of user interest over-specialization. An algorithm is proposed to fully mine and use the implicit user interest information for recommendation. The probabilistic topic model is adopted to extract user interest distribution, and the weighted tripartite graph of user-interest-item is generated. Then the user item resource value is allocated by material diffusion algorithm in user-interest and interest-item bipartite graphs respectively, and the Top-K recommendation list is generated according to the rank of item resource values. Experimental results on Movielens datasets show the proposed algorithm relieves the problem of user interest over-specialization. Meanwhile the recommendation accuracy is improved .