Using Collaborative Based Algorithm for Efficient Management of Limited Resources on Social Networks

Abstract

With all features and resources, such as: social actors, social relations, content, communication, and ratings that todays' social networks like Facebook, LinkedIn, Twitter, Google+, etc. offer to users, it still appears that at given point we have to refine and optimize our own accounts within the limits of a certain social network. In line with this trend, in this paper we present a model for efficient management of friends list in Facebook, as one of the limited resource in this social network. In order to get users data from Facebook, a web scraping technique combined with reverse image search has been adopted to ensure users authenticity. The activity between nodes (friends) on a social network is calculated based on their interactions in terms of likes, comments, shares and posts between each other. This approach led us into designing and implementing an algorithm based in these collaborative metrics, named "weight of relationship". This algorithm calculates weights between friends on a network, and the results are evaluated by comparing these weights with respondent answers, conducted through personalized questionnaire. Consequently, this methodology brings feasible results, with an average accuracy of 71% on recommending which friends should be removed, thus releasing the space for incoming new friends. An app named RateMyFriends is developed based on presented approach.