As online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the More
As online social networks become more widely used, there is a growing focus on the role of recommender algorithms within these platforms. It is important to assess the accuracy of these algorithms in providing suitable recommendations. Our research demonstrates that the presence of individuals and acquaintances within social networks influences user behavior in ways that are largely psychological. Many user actions on a post are influenced by their respect or closeness to the post's owner. This article explores how the predictability of user behavior towards posts from friends and acquaintances highlights the impact of emotional connections stemming from stable social relationships on post acceptance. It also raises concerns about the potential for incorrect recommendations in algorithms based on collaborative filtering due to data bias caused by these factors.
Manuscript profile
Recommendation systems propose specific items to users based on their interests by analysis the user data. The main goal of this analysis is extraction of each user pattern to predict the interested items. One of the main well-known methods in recommender systems is col More
Recommendation systems propose specific items to users based on their interests by analysis the user data. The main goal of this analysis is extraction of each user pattern to predict the interested items. One of the main well-known methods in recommender systems is collaborative filtering in which similarity measures are utilized to detect similar users to a new user. The challenging issues related to collaborative filtering are similarity and neighborhood detection. In this paper, nearest neighbor (NN) algorithm is used to detect similar neighbors to a new user. The proposed model, which is inspired by user-item method, the score of items is calculated based on a distance metric and the nearest neighbor is selected. In the presented work, we detect similar users using user-item matrix and the Euclidean distance. The proposed method is evaluated on Movielens dataset which includes 1682 items and evaluation metrics such as Accuracy, Precision, Recall, F1-measure, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are measured. MAE of the proposed method is 0.7351 which is less than Pearson and Cosine similarities, which demonstrates the superior performance of the proposed method in similarity detection and prediction.
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Introduction
There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance be More
Introduction
There are many improvements in insurance industries in these decades. So Many people refer to public and private insurance companies to get insurance services. They usually face to some challenges and issues for selecting the best and suitable insurance because of various type of insurance and lack of enough information of insurance service. Choosing the proper insurance service always related to people personal and social features
Method
Prediction of customer’s insurance selection according to people personal and social property especially thier financial condition play vital role. On one hand Prediction of insurance type can help people who want to utilize insurance service. On the other hand this prediction can facilitate process of insurance for Insurers too. There are multiple important mechanisms and factors like customers clustring, analyze each class feature, detection of popular insurance in each class and using Collaborative filtering technique to offer best insurance that can influence on process of decision and selection the suitable insurance.
Results
The total precision value of the proposed method is 89.98% for joint insurances of similar users. Also, the total value of the F-measure of the proposed method for joint insurances between similar customers is 87.13%.
Discussion
Customer behavior can be predicted by available data of people’s personal and social features and type of insurance that they are chosen and rate of their satisfactions. K-means clustring algorithm and recommender systems Techniques like Collaborative filtering are two significant mechanisms to implement prediction of customer’s behaviors.
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