Zahra Shirani; Amir Jalaly Bidgoly
Abstract
In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from ...
Read More
In recent years, the number of users of social networks has grown significantly. The big challenge for these networks’ audience is How to communicate with the people present on these networks. Friend recommender systems try to fix this challenge by offering suggestions. In this study, data from the social and scientific network of Kousarent were used. In this research, using 10 types of relationships between users without considering the friendship relationships, network graph created, and then by using 3 algorithms Louvain, Kmeans and Hierarchical graph clustering was performed to identify communities. Clusters obtained from Louvain's clustering algorithm had higher percentages of matching with friendships. Then, weights were calculated by genetic algorithm for each of 10 relationships and by applying Louvain clustering algorithm on the network graph, the highest percentage of matching with the optimal weight of each of the 10 relationships was obtained. In this case, the resulting clusters are optimal clusters containing the most similar users. So other users in the same cluster can be suggested as friends. The weight of the edges between the individuals in the graph was also used to prioritize the bids. At the end, the friend proposed method was evaluated and the percentage of suggested friends matched with the individual's true friends was calculated.
Leila Ebrahimi; Vahid Reza Mirabi; Mohammad Hossein Ranjbar; Esmaeil Hassan Pour
Abstract
The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the ...
Read More
The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the developed model, a questionnaire survey is conducted and the data is collected from the 384 customers in a B2C website. We used SPSS and SmartPLS software for descriptive statistics and path analyses and to verify the proposed model. The result of the Structural Equations Modeling showed that trust has a significant relationship with the customers’ satisfaction in the e-commerce recommendation systems. In addition, the results revealed that satisfaction with the recommended products can improve the customers’ loyalty in the B2C recommendation systems. The proposed model will help the e-commerce managers to improve their website recommendation systems and increase the sale of the products by achieving the customers’ loyalty in the online shopping websites.
