In social networks,user attention affects the user’s decision-making,resulting in a performance alteration of the recommendation systems.Existing systems make recommendations mainly according to users’preferences wi...In social networks,user attention affects the user’s decision-making,resulting in a performance alteration of the recommendation systems.Existing systems make recommendations mainly according to users’preferences with a particular focus on items.However,the significance of users’attention and the difference in the influence of different users and items are often ignored.Thus,this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks.We first constructed the basic user and item matrix via convolutional neural networks(CNN).Then,we obtained user preferences by using the relationships between users and items,which were later inputted into our model to learn the preferences between friends.The error performance of the proposed method was compared with the traditional solutions based on collaborative filtering.A comprehensive performance evaluation was also conducted using large-scale real-world datasets collected from three popular location-based social networks.The experimental results revealed that our proposal outperforms the traditional methods in terms of recommendation performance.展开更多
文摘In social networks,user attention affects the user’s decision-making,resulting in a performance alteration of the recommendation systems.Existing systems make recommendations mainly according to users’preferences with a particular focus on items.However,the significance of users’attention and the difference in the influence of different users and items are often ignored.Thus,this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks.We first constructed the basic user and item matrix via convolutional neural networks(CNN).Then,we obtained user preferences by using the relationships between users and items,which were later inputted into our model to learn the preferences between friends.The error performance of the proposed method was compared with the traditional solutions based on collaborative filtering.A comprehensive performance evaluation was also conducted using large-scale real-world datasets collected from three popular location-based social networks.The experimental results revealed that our proposal outperforms the traditional methods in terms of recommendation performance.
