Social Curation Networks Oriented Social Recommendation Algorithm Research

Recently,online social curation networks attract lots of users due to its convenience to retrieve,collect,sort and share multimedia content with each other.The studies of recommendation on social curation networks of complex information environment becomes an urgent problem for the development of social networks.To extract the latent information for users from huge amount of information is a challenge problem for recommendation.In this theses,infer the correspondence between users and images,according to user's requirement to recommend images for users in Huaban.com.The main contributions are summarized as follows:1.We implement a word2 vec based image recommendation algorithm.First,we employ given relations between users and images to form user-image sequences.Then,we leverage word2 vec model to extract the features of users and images,and embed them into a uniform feature space.Finally,similarity of users and images are measured in the feature space for ranking the relevance of users and images,where the first ones are recommended for users.2.We propose a content-based bipartite graph algorithm,and involve the visual content relations of images to assist bipartite graph for social recommendation.Bipartite graph employs resource-allocation to recommend images by given relations of users and images with considering visual preference.In this work,we propose a correlation analysis method,which involve visual content of images into bipartite graph to extend scope and promote effectiveness of recommendation.In addition,content similarity is employed for recommendation reranking to improve visual quality of recommended images.Experimental results demonstrate that the proposed method enhanced the performance with effectively visual content.3.We propose a hybrid graph model for image recommendation.The corresponding intrinsic hierarchical information structure of social curation networks are adopted to construct the hybrid graph model.And the transition probability of different relations are constitute the transition probability matrix.Then randomwalk are performed to recommend images over the transition probability.Furthermore,we combine the content similarity of images with hybrid graph model to promote the transition probability matrix.Experimental results illustrate the effectiveness of the hybrid graph model.