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Network embedding has been recently used in social network recommendations by embedding low-dimensional representations of network items for recommendation. However, existing item recommendation models in social networks suffer from two limitations. First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information. Second, network embedding and item recommendations are learned in twodoi:10.24963/ijcai.2018/469 dblp:conf/ijcai/GaoYWZLH18 fatcat:63fvrois7ffsfk2ojwp3yeymly