CAJ | 학술논문

基于社会化标签的个性化推荐已成为推荐领域关注的热点问题，但面临着用户信息丢失、时间效应和用户兴趣迁移等一系列挑战。文中基于用户行为数据建立用户-物品-标签完全三部图模型，并基于此提出个性化物品推荐算法。该方法首先对用户兴趣动态迁移现象进行分析，其次综合考虑用户-物品-标签三者关系，提出了完全三部图模型，接着引入时间加权连接权重来构建新的连接关系矩阵，最后在此基础上运行MassDiffusion推荐算法，通过综合两个方向的物质扩散来获得推荐结果。实验结果表明，文中算法能够通过反映用户兴趣的动态迁移，有效地提高推荐的准确性和多样性。
기우사회화표첨적개성화추천이성위추천영역관주적열점문제，단면림착용호신식주실、시간효응화용호흥취천이등일계렬도전。문중기우용호행위수거건립용호-물품-표첨완전삼부도모형，병기우차제출개성화물품추천산법。해방법수선대용호흥취동태천이현상진행분석，기차종합고필용호-물품-표첨삼자관계，제출료완전삼부도모형，접착인입시간가권련접권중래구건신적련접관계구진，최후재차기출상운행MassDiffusion추천산법，통과종합량개방향적물질확산래획득추천결과。실험결과표명，문중산법능구통과반영용호흥취적동태천이，유효지제고추천적준학성화다양성。
Personalized recommendation based on social tagging has become a key research topic in the field of recommendation. Current recommending methods,however,are facing a series of challenges,such as the loss of user information,the effect of time and user interest migration. A new personalized recommendation algorithm based on user-item-tag complete tripartite graph model derived from the user behavior is proposed. Firstly,research on dynamic migration of user interest is carried out. Secondly,user-item-tag complete tripartite graph model is proposed with comprehensive consideration of user-item-tag relationships. Time-weighted connections is employed to construct the new connection matrix. Finally,MassDiffusion algorithm is executed to carry out personalized recommendation based on the model through combining two directions of mass diffusion. Experimental results demonstrate that the algorithm can effectively improve the accuracy and diversity of recommendation through reflecting the dynamic migration of user interest.