Trust-Based Rating Prediction and Malicious Profile Detection in Online Social Recommender Systems

Online social networks and recommender systems have become an effective channel for influencing millions of users by facilitating exchange and spread of information. This dissertation addresses multiple challenges that are faced by online social recommender systems such as: i) finding the extent of information spread; ii) predicting the rating of a product; and iii) detecting malicious profiles. Most of the research in this area do not capture the social interactions and rely on empirical or statistical approaches without considering the temporal aspects. We capture the temporal spread of information using a probabilistic model and use non-linear differential equations to model the diffusion process. To predict the rating of a product, we propose a social trust model and use the matrix factorization method to estimate user\u27s taste by incorporating user-item rating matrix. The effect of tastes of friends of a user is captured using a trust model which is based on similarities between users and their centralities. Similarity is modeled using Vector Space Similarity and Pearson Correlation Coefficient algorithms, whereas degree, eigen-vector, Katz, and PageRank are used to model centrality. As rating of a product has tremendous influence on its saleability, social recommender systems are vulnerable to profile injection attacks that affect user\u27s opinion towards favorable or unfavorable recommendations for a product. We propose a classification approach for detecting attackers based on attributes that provide the likelihood of a user profile of that of an attacker. To evaluate the performance, we inject push and nuke attacks, and use precision and recall to identify the attackers. All proposed models have been validated using datasets from Facebook, Epinions, and Digg. Results exhibit that the proposed models are able to better predict the information spread, rating of a product, and identify malicious user profiles with high accuracy and low false positives

The Effectiveness of training of self-help program toward the parenthood on worry in pregnancy period among the nulliparous women

Background and objective: accept of parenting duty needs the psychological and emotional readiness. This study aimed to investigate the effectiveness of training of self-help program toward the parenthood on worry in pregnancy period among the nulliparous women. Methods:The research method of present study was semi-experimental research with pre-posttest with control group. The study population included 30 nulliparous women who were referred to the health center in 3 phase of Andisheh city, Tehran by 2017. The women were selected by simple randomized sampling and were assigned to two groups (each group was 15 women). The experimental group received nine session of self-help program toward the parenthood. The information were collected by prenatal distress questionnaire (PDQ) made by Alderdice et al (2011). The obtained data were analyzed using ANCOVA. The data utilized by SPSS version 21. Results: The ANCOVA analysis of the data showed that the experimental group had Significant decrease in factor 1 concerns about birth and the baby( F=48.689,

Effects Of User Interactions On Online Social Recommender Systems

We analyze online social data to model social interactions of users in recommender systems: i) Rating prediction, and ii) detecting spammers and abnormal user rating behaviors. We propose a social trust model using matrix factorization method to estimate users taste by incorporating user-item matrix. The effect of users friends tastes is modeled based on centrality metrics and similarity algorithms between users. The proposed method is validated using Epinions Dataset. To identify abnormal users in social recommender systems, we propose a classification approach. We define attributes to provide likelihood of a user having a profile of that of an attacker. Using user-item rating matrix and user-connection matrix, we find if the ratings are abnormal and if connections are random. We use k-means clustering to categorize users into authentic users and attackers. We use Epinions dataset to test the profile injection attacks

Product Rating Prediction Using Centrality Measures In Social Networks

Online recommendation systems provide useful information to users on various products and also allow the users to rate the products. However, they do not usually consider the fact that users trust their connections more than others and that the trusts vary from connection to connection i.e., we value the opinions of our connections differently. Moreover, the importance of connections\u27 opinion changes over time. Thus, there is a need to consider the evolving trust relationships among users. In this work, we use both the user\u27s social connections and non-connections to predict how a user would rate a particular product. We argue that we not only trust our connections more but also the trust varies over time, which we capture using a time-dependent trust matrix. We use the degree and eigen-vector centrality measures in conjunction with the user-item rating matrix to find how the social connections impact how one rates a product. To test the validity of the proposed framework, we use Epinions dataset which provides the ratings for products and trust matrix over 11 time periods. We show the accuracy our predictive model using the mean absolute error

Detection Of Profile Injection Attacks In Social Recommender Systems Using Outlier Analysis

As systems based on social networks grow, they get affected by huge number of fake user profiles. Particularly, social recommender systems are vulnerable to profile injection attacks where malicious profiles are injected into the rating system to affect user\u27s opinion. The objective of attackers is to inject a large set of biased profiles that provide favorable or unfavorable recommendations for a product. In this paper, we propose a classification technique for detection of attackers. First, we define the attributes that provide the likelihood of a user having a profile of that of an attacker. Using user-item rating matrix, user-connection matrix, and similarity between users, we find if the ratings are abnormal and if there are random connections in the network. Then, we use fc-means clustering to categorize users into authentic users and attackers. To evaluate our framework, we use Epinions dataset and inject intelligent push and nuke attacks. These attacks make arbitrary connections to existing users and provide biased ratings. To evaluate the performance, we use precision and recall to show that fc-means clustering can identify the attackers with high accuracy and low false positives

Social Trust Model For Rating Prediction In Recommender Systems: Effects Of Similarity, Centrality, And Social Ties

The success of e-commerce companies is becoming increasingly dependent on product recommender systems which have become powerful tools that personalize the shopping experience for users based on user interests and interactions. Most modern recommender systems concentrate on finding the relevant items for each user based on their interests only, and ignore the social interactions among users. Some recommender systems, rely on the ‘trust’ of users. However in social science, trust, as a human characteristic, is a complex concept with multiple facets which has not been fully explored in recommender systems. In this paper, to model a realistic and accurate recommender system, we address the problem of social trust modeling where trust values are shaped based users characteristics in a social network. We propose a method that can predict rating for personalized recommender systems based on similarity, centrality and social relationships. Compared with traditional collaborative filtering approaches, the advantage of the proposed mechanism is its consideration of social trust values. We use the probabilistic matrix factorization method to predict user rating for products based on user-item rating matrix. Similarity is modeled using a rating-based (i.e., Vector Space Similarity and Pearson Correlation Coefficient) and connection-based similarity measurements. Centrality metrics are quantified using degree, eigen-vector, Katz and PageRank centralities. To validate the proposed trust model, an Epinions dataset is used and the rating prediction scheme is implemented. Comprehensive analysis shows that the proposed trust model based on similarity and centrality metrics provide better rating prediction rather than using binary trust values. Based on the results, we find that the degree centrality is more effective compared to other centralities in rating prediction using the specific dataset. Also trust model based on the connection-based similarity performs better compared to the Vector Space Similarity and Pearson Correlation Coefficient similarities which are rating based. The experimental results on real-world dataset demonstrate the effectiveness of our proposed model in further improving the accuracy of rating prediction in social recommender systems

Product Rating Prediction Using Trust Relationships In Social Networks

Traditional recommender systems assume that all users are independent and identically distributed, and ignores the social interactions and connections between users. These issues hinder the recommender systems from providing more personalized recommendations to the users. In this paper, we propose a social trust model and use the probabilistic matrix factorization method to estimate users taste by incorporating user-item rating matrix. The effect of users friends tastes is modeled using a trust model which is defined based on importance (i.e., centrality) and similarity between users. Similarity is modeled using Vector Space Similarity (VSS) algorithm and centrality is quantified using two different centrality measures (degree and eigen-vector centrality). To validate the proposed method, rating estimation is performed on the Epinions dataset. Experiments show that our method provides better prediction when using trust relationship based on centrality and similarity values rather than using the binary values. The contributions of centrality and similarity in the trust values vary with different measures of centrality

Probabilistic Spreading Of Recommendations In Social Networks

In this paper, we study how the recommendation of a product spreads across a social network assuming all members of the network recommend the product to their neighbors in a probabilistic manner. To do so, we consider a social network which is typically characterized by a scale-free network obeying power-law degree distribution. We take a layer-by-layer approach where nodes are labeled by how far they are from the origin node. Starting with the layer-1 nodes, we first compute the probability when the recommendation propagates outward from origin node considering the out-degree distribution. Then, we compute the probabilities when recommendations are made from nodes that are farther from the origin to nodes that are closer to the origin. Also, using the concept of clustering coefficient, we consider the recommendation probabilities within the same layer. Combining different possibilities, we are able to find the total effect. In order to demonstrate how recommendation spreads, we use Facebook data from SNAP and show how many nodes receive the recommendation in each layer and what the effect of the location of a node is with respect to the origin node

Prediction Of Information Diffusion In Social Networks Using Dynamic Carrying Capacity

Online social networks have become an effective channel for influencing millions of users by facilitating exchange and spread of information. Despite recent works on modeling information diffusion in social networks, the complexity of social interactions makes quantification of any spreading phenomenon in social networks a challenging task. Most of the research in this area rely on empirical or statistical approaches without considering the temporal aspects and the carrying capacity of the networks. In this paper, we capture the temporal evolution of information spread in a social network using linear ordinary differential equations (ODEs). Our proposed model shows the influence of users and their temporal actions on the carrying capacity. We validate the diffusion process across the network using a dataset collected from Digg which is a popular social news sharing website. The results show that our dynamic carrying capacity PDE model is able to predict with high accuracy how the information diffuses in the network during the different phases of the lifetime of a news story. We also propose a model to represent the carrying capacity based on the portion of the influenced users