将用户社会关系引入用户-商品评分数据中,构建用户-商品异构关系图,可缓解传统推荐系统面临的数据稀疏性和冷启动问题.但是,由于用户间社会关系的复杂性,聚合不一致的社会邻居可能会降低推荐性能.针对上述问题,文中提出基于自监督三重...将用户社会关系引入用户-商品评分数据中,构建用户-商品异构关系图,可缓解传统推荐系统面临的数据稀疏性和冷启动问题.但是,由于用户间社会关系的复杂性,聚合不一致的社会邻居可能会降低推荐性能.针对上述问题,文中提出基于自监督三重训练和聚合一致邻居的社会化推荐模型(Social Recommendation Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation,SR-STCNA).首先,在用户-商品评分数据的基础上,引入用户-用户间的社交关系,在用户-商品异构图中构建多种关系.使用超图表示用户和用户、用户和商品之间的关系.使用自监督三重训练,从未标记的数据中学习用户表示,充分挖掘用户-用户和用户-商品间存在的复杂连接关系.然后,通过用户-商品异构图上的节点一致性得分和关系自注意力,在用户和商品表示学习过程中聚合一致邻居,增强用户和商品嵌入表示能力,提高推荐性能.在CiaoDVD、FilmTrust、Last.fm、Yelp数据集上的实验表明,SR-STCNA性能较优.展开更多
The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the intera...The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.展开更多
该文在奇异值矩阵分解方法的基础上,提出了一种融合景点季节演变信息的旅游推荐算法。该算法根据景点属性与季节演变之间的关联,将旅游景点的属性划分为静态方面和动态方面,并通过设计包含时间因素的动态偏置函数来刻画用户偏好与景点...该文在奇异值矩阵分解方法的基础上,提出了一种融合景点季节演变信息的旅游推荐算法。该算法根据景点属性与季节演变之间的关联,将旅游景点的属性划分为静态方面和动态方面,并通过设计包含时间因素的动态偏置函数来刻画用户偏好与景点之间的动态关联。这些静态和动态方面的信息被作为新的偏置项融入有偏奇异分解(Bias singular value decomposition,Bias SVD)模型,以改善用户对旅游景点的评分预测。标准数据集Yelp上的实验结果表明,相比于对用户签到数据无差别对待的推荐方法,该文方法在推荐精度和用户体验方面均有明显的提升。展开更多
文摘将用户社会关系引入用户-商品评分数据中,构建用户-商品异构关系图,可缓解传统推荐系统面临的数据稀疏性和冷启动问题.但是,由于用户间社会关系的复杂性,聚合不一致的社会邻居可能会降低推荐性能.针对上述问题,文中提出基于自监督三重训练和聚合一致邻居的社会化推荐模型(Social Recommendation Based on Self-Supervised Tri-Training and Consistent Neighbor Aggregation,SR-STCNA).首先,在用户-商品评分数据的基础上,引入用户-用户间的社交关系,在用户-商品异构图中构建多种关系.使用超图表示用户和用户、用户和商品之间的关系.使用自监督三重训练,从未标记的数据中学习用户表示,充分挖掘用户-用户和用户-商品间存在的复杂连接关系.然后,通过用户-商品异构图上的节点一致性得分和关系自注意力,在用户和商品表示学习过程中聚合一致邻居,增强用户和商品嵌入表示能力,提高推荐性能.在CiaoDVD、FilmTrust、Last.fm、Yelp数据集上的实验表明,SR-STCNA性能较优.
文摘The introduction of the social networking platform has drastically affected the way individuals interact. Even though most of the effects have been positive, there exist some serious threats associated with the interactions on a social networking website. A considerable proportion of the crimes that occur are initiated through a social networking platform [1]. Almost 33% of the crimes on the internet are initiated through a social networking website [1]. Moreover activities like spam messages create unnecessary traffic and might affect the user base of a social networking platform. As a result preventing interactions with malicious intent and spam activities becomes crucial. This work attempts to detect the same in a social networking platform by considering a social network as a weighted graph wherein each node, which represents an individual in the social network, stores activities of other nodes with respect to itself in an optimized format which is referred to as localized data set. The weights associated with the edges in the graph represent the trust relationship between profiles. The weights of the edges along with the localized data set are used to infer whether nodes in the social network are compromised and are performing spam or malicious activities.
文摘该文在奇异值矩阵分解方法的基础上,提出了一种融合景点季节演变信息的旅游推荐算法。该算法根据景点属性与季节演变之间的关联,将旅游景点的属性划分为静态方面和动态方面,并通过设计包含时间因素的动态偏置函数来刻画用户偏好与景点之间的动态关联。这些静态和动态方面的信息被作为新的偏置项融入有偏奇异分解(Bias singular value decomposition,Bias SVD)模型,以改善用户对旅游景点的评分预测。标准数据集Yelp上的实验结果表明,相比于对用户签到数据无差别对待的推荐方法,该文方法在推荐精度和用户体验方面均有明显的提升。
