IMPROVING FRIENDS RECOMMENDATION USING FP-GROWTH ALGORITHM IN SOCIAL TAGGING SYSTEM

Abstract

Social tagging system are web-based sites that store user’s keywords called tags, continue to receive signiï¬cant consideration in academic environment it became an interesting research topic, give good support for users to tag resources, communicate with friends as well as friend recommendation, due to increasingly acceptance of Web2.0. There is a constant growth in the number of users using social tagging. Friend recommendation is one of the most important aspects for overcoming information overloading problem and helps users to make choice. In this work we proposed improving friend recommendation using, to address the difficulty of tag ambiguities. The technique is consisted of the following stages: Apply FP-Growth algorithm to discovery the frequent events sets among the users, construct the combine trust graph, and then apply ant colony optimization Algorithm to joint trust graph to compute the optimal friend recommended through repetition. The results of our experiments on a Delicious dataset, using our model, precision improved in the range 0.1 % to 1.1 %, recall increased greatly in the range 21.85 % to 50.84 % and F₂ increased in the range 31.31 % to 50.85 % approximately. When compared to other methods, the outcome demonstrates a significant improvement. For friend commendation in a social tagging system, the future approach is to apply various data mining methods, large scale datasets, and community detection.