Network embedding enhanced intelligent recommendation for online social networks

Abstract

With the fast development of Internet technology, more and more online social networks are changing our daily life. Whether or not an accurate recommendation can be provided for each user in the massive amount of information directly affects the user’s enthusiasm for receiving network services and the user experience effect, which in turn determines the user’s participation and loyalty to network applications. However, most previous methods only use a single network topology information and ignore other auxiliary information (such as user content information). Moreover, how to deal with large scale network is a challenging task. To tackle these challenges, we propose a topic-aware network embedding approach for providing intelligent recommendation services. Specifically, we first extract the network topology based on the constructed social network. Then, we extract the topic information based on the context released by the users with the help of topic model. Finally, a topic-aware network embedding framework is utilized for recommendation. Experimental results on two-widely used dataset demonstrate that our method can achieve the best performance.