Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

Kefei Cheng,Xiaotong Cui,Xiaoyong Guo,Fengchi Shan

doi:10.1155/2020/3273451

Abstract

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

Highlights

The social network services composed of online information flow have a huge number of users and accumulate a large amount of information data due to the active online behaviors of users
According to a report of 2012 by the International Data Group (IDC), by 2020, the total global data are expected to be 22 times those of 2011, reaching 35.2 ZB [1]. In these social networks composed of information flow services, information is usually disseminated probabilistically according to the topology structure of the social network
These huge information data make the spread of information in the social network become congested, and a large amount of information cannot be browsed. e direct consequence to users is information overload. e recommendation algorithm can break the limitations of the traditional social network topology and enhance the spread of information in the social network and improve the efficiency of obtaining information for multiple users and solve the problem of information overload. erefore, personalized recommendation technology has become an important topic of common concern in academia and industry today

Summary

Introduction

The social network services composed of online information flow have a huge number of users and accumulate a large amount of information data due to the active online behaviors of users. In response to the above problems, it is very important to find a new method to model user interaction data for information recommendation in social network services. Ough the content-based recommendation algorithm has obtained a good result, the sparsity existing in user behavior data will affect the accuracy of recommendation to a certain extent. E main work is to improve the word embedding model by adding clicking list as global context and use the improved model as the feature extractor of the content-based recommendation algorithm. E goal of constructing this dataset is to learn a d-dimensional representation for each browsing list li by using the word embedding model, that is, to learn the representations of browsing data using the Skip-gram model by maximizing the objective function L on the entire dataset S. e objective function is defined as follows: Input. As we can see from (1) and (2), the context of user browsing sequences is modeled. e result is that browsing lists with similar contexts will have similar embedded representations

Calculated similarity e similarity of the data is calculated

Input layer

Experimental Results and Evaluation

Statistical indicators

RPEW DLRASN