为了解决就医过程中医疗资源短缺和患者时间不充裕、行程不便的问题,提出了结合外部知识的基于记忆网络的知识感知医疗对话生成模型(memory networks based knowledge-aware medical dialogue generation model,MKMed).该模型首先通过...为了解决就医过程中医疗资源短缺和患者时间不充裕、行程不便的问题,提出了结合外部知识的基于记忆网络的知识感知医疗对话生成模型(memory networks based knowledge-aware medical dialogue generation model,MKMed).该模型首先通过利用精确字匹配的方法在对话历史中进行实体追踪;随后在外部实体知识数据库里设计2阶段的实体预测,筛选出可能出现在回复中的医疗实体及对应知识,其中2阶段实体预测分别利用计算共现矩阵和余弦相似度的方法;模型接着用记忆网络来存储知识和对话历史的信息;最后整合记忆网络存储的信息,并使用注意力机制以及循环神经网络生成回复.在带有外部知识的大规模医疗对话数据集KaMed上进行了相关实验,该数据集为收集自在线平台的真实数据.实验结果表明提出的模型生成的回复在流畅性、多样性、正确性和专业性等方面均显著优于大部分基准模型.证明了合理引入外部知识的医疗对话模型能产生成更有医疗价值的回复.展开更多
Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most ca...Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption -- a user＇s taste is close to the neighbors he/she trusts into the Bayesian Personalized Ranking model. To explore the impact of users＇ multi-faceted trust relations, we further propose a category- sensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method SocialBPRcawn by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method SocialBPRcawR in terms of AUC （area under the receiver operating characteristic curve）.展开更多
文摘为了解决就医过程中医疗资源短缺和患者时间不充裕、行程不便的问题,提出了结合外部知识的基于记忆网络的知识感知医疗对话生成模型(memory networks based knowledge-aware medical dialogue generation model,MKMed).该模型首先通过利用精确字匹配的方法在对话历史中进行实体追踪;随后在外部实体知识数据库里设计2阶段的实体预测,筛选出可能出现在回复中的医疗实体及对应知识,其中2阶段实体预测分别利用计算共现矩阵和余弦相似度的方法;模型接着用记忆网络来存储知识和对话历史的信息;最后整合记忆网络存储的信息,并使用注意力机制以及循环神经网络生成回复.在带有外部知识的大规模医疗对话数据集KaMed上进行了相关实验,该数据集为收集自在线平台的真实数据.实验结果表明提出的模型生成的回复在流畅性、多样性、正确性和专业性等方面均显著优于大部分基准模型.证明了合理引入外部知识的医疗对话模型能产生成更有医疗价值的回复.
基金the National Natural Science Foundation of China under Grant Nos. 61272240, 60970047, 61103151 and 71301086, the Doctoral Fund of Ministry of Education of China under Grant No. 20110131110028, the Natural Science Foundation of Shandong Province of China under Grant No. ZR2012FM037, and the Excellent Middle-Aged and Youth Scientists of Shandong Province of China under Grant No. BS2012DX017.
文摘Social trust aware recommender systems have been well studied in recent years. However, most of existing methods focus on the recommendation scenarios where users can provide explicit feedback to items. But in most cases, the feedback is not explicit but implicit. Moreover, most of trust aware methods assume the trust relationships among users are single and homogeneous, whereas trust as a social concept is intrinsically multi-faceted and heterogeneous. Simply exploiting the raw values of trust relations cannot get satisfactory results. Based on the above observations, we propose to learn a trust aware personalized ranking method with multi-faceted trust relations for implicit feedback. Specifically, we first introduce the social trust assumption -- a user＇s taste is close to the neighbors he/she trusts into the Bayesian Personalized Ranking model. To explore the impact of users＇ multi-faceted trust relations, we further propose a category- sensitive random walk method CRWR to infer the true trust value on each trust link. Finally, we arrive at our trust strength aware item recommendation method SocialBPRcawn by replacing the raw binary trust matrix with the derived real-valued trust strength. Data analysis and experimental results on two real-world datasets demonstrate the existence of social trust influence and the effectiveness of our social based ranking method SocialBPRcawR in terms of AUC （area under the receiver operating characteristic curve）.
