Abstract
With the rising popularity of social networks and service recommendations, research on new methods of friend recommendation have become a key topic, especially when based on quality-driven resource processing in an edge computing environment. Traditional methods seldom systematically combine static attributes (e.g., interests, geographical locations, and common friends), dynamic behaviors (e.g., liking, making comments, forwarding and @), and network structures (e.g., social ties) to recommend a new friend to a target user. Meanwhile, with the advent of deep learning, it has become more challenging to integrate these features into a deep neural network framework for friend recommendation. For example, how do we optimally make use of these features to form a united framework and what type of deep neural network architecture should be introduced into a novel recommendation method in an edge computing environment? In this paper, we propose DFRec++, a hybrid deep neural network framework combining attribute attention and network embeddings to make social friend recommendations with the help of both interactive semantics and contextual enhancement. More specifically, we first utilize the latent dirichlet allocation (LDA) topic model to generate common interest topics between users and compute the similarity of the explicit static attribute vector representation of topics, locations, and common friends. Then we feed dynamic behavior attributes into a convolutional neural network (CNN) to obtain the implicit vector representation of the interactions and context between two users. Subsequently, a multi-attention mechanism is designed to further improve the deep vector representation of the attribute information. Next, the LINE-based network embeddings algorithm is applied to embed the network structure into a low-dimensional vector. Finally, the attribute attention vector and the network embeddings are concatenated to form a deep feature representation, which is subsequently fed to a fully connected neural network (FCNN) to capture the probability of friendship between two users. The output of FCNN indicates the probability of two users becoming friends. We conducted experiments on a real-world Weibo dataset and the results show that DFRec++ outperforms several existing methods.
Highlights
In the last few years, social networks, such as for example Weibo and Facebook have been growing exponentially.The associate editor coordinating the review of this manuscript and approving it for publication was Honghao Gao .One of the critical tasks in social networks is friend recommendation
2) We propose a novel hybrid deep neural network architecture, which consists of convolutional neural network (CNN) and fully connected neural network (FCNN)
By combining attribute attention and network embedding, the performance increased by 53% and 41% as compared with DFRec++-AA and DFRec++NE, respectively
Summary
In the last few years, social networks, such as for example Weibo and Facebook have been growing exponentially. In addition to static information, which includes profiles, the text of participants’ posts and their geographical locations, and user interactions such as liking and forwarding are worth taking into account It is important for friend recommendation models to learn implicit feature interactions behind user click behaviors. The users’ topics and dynamic behaviors are processed using the LDA and CNN, and attention is used to represent the users’ static attribute information This framework, lacks the characteristic information of social networks. We systematically investigated how to optimally integrate the features representing both static attributes and dynamic behaviors for a good performance in friend recommendation. 1) To our knowledge, we are the first researchers to systematically combine static attributes, dynamic behaviors, and network structures to make friend recommendations using a deep learning framework.
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