Click-Through Rate Prediction Network Based on User Behavior Sequences and Feature Interactions

In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.

Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

Using a behavior random permutation model to identify displacement grooming in ungulates

Behavior affects an individual's life in all aspects,e.g.,enhancing fitness,leveraging predation risk,and reducing competition with conspecifics.However,the sequential distribution of behaviors received less attention and is unclear what the function of displacement behavior is.Displacement activities can be found in vertebrate species but there is no formal method to determine whether a behavior is expressed as a displaced or normal activity.Analyzing the sequential distributions of behaviors in a natural setting may allow researchers to identify unexpected distributions as a possible signature of displacement activities.In this study,we used a behavior random permutation model to detect the presence of a displacement activity in the Tibetan antelope Pantholops hodgsonii and the Tibetan gazelle Procapra picticaudata.The results showed that grooming in both ungulates tended to be accompanied with vigilance,and the frequency of grooming after vigilance was significantly higher than before vigilance.A significant positive correlation between the scan rate and grooming rate in the 2 ungulates was obtained.We suggest that grooming could sometimes be expressed as a displacement activity in ungulates.In addition to providing a general method for further research on displacement activities in a variety of animal species,this study sheds light on the importance of a spectral analysis of sequential distribution of animal behaviors.Behavior random permutation models can be used to explore the relevance between any 2 behaviors in a specific sequence,especially to identify a myriad of unexpected behaviors relative to their normal context of occurrence.