Heterogeneous Social Recommendation Model With Network Embedding
نویسندگان
چکیده
منابع مشابه
Heterogeneous Information Network Embedding for Recommendation
Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommenda...
متن کاملHINE: Heterogeneous Information Network Embedding
Network embedding has shown its effectiveness in embedding homogeneous networks. Compared with homogeneous networks, heterogeneous information networks (HINs) contain semantic information from multi-typed entities and relations, and are shown to be a more effective model for real world data. The existing network embedding methods fail to explicitly capture the semantics in HINs. In this paper, ...
متن کاملAttributed Social Network Embedding
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship n...
متن کاملA Hybrid Microblog Recommendation Model in Mobile Social Network
Recently, microblogs have emerged as a new open channel of communication for people on the Internet to read, commentate, socialize and so on. With the advent of a huge number of information in microblog spaces, including articles, profile, pictures and other multimedia resources, the “information overload” has become a critical problem for microblog users, which brings bloggers plethora of choi...
متن کاملGraph Embedding with Rich Information through Bipartite Heterogeneous Network
Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding only rely on the typology information and fail to use the copious information in nodes as well as edges. As a result, their performance for many tasks may not be satisfactory. In this paper, we proposed a novel and general framework of repre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3038022