Abstract
Advice seeking and knowledge exchanging over the Internet and social networks became a very common activity. The system proposed in this work aims to assist the users in choosing the best possible advice and allows them to exchange advice automatically without knowing each other. The approach used in this work is based on a newly proposed dynamic version of the hidden metric model, where the distance between each couple of users is computed and used to represent the users in a d dimensional Euclidean space. In addition to the position, a degree is also assigned to each user, which represents his/her popularity or how much he/she is trusted by the system. The two factors, distance and degree, are used in selecting advice providers. Both the positions of the users and their degrees are adjusted according to the feedback of the users. The proposed feedback algorithm is based on a Bayesian framework and has a goal of obtaining more accurate advice in the future. The system evaluated and tested using simulation. In the applied experiment, the mean square error was measured for different parameters. All parts of the experiments are performed on a varying number of users (100, 500 and 1000 users). This shows that the system can scale to a large number of users.