A user ranking algorithm for efficient information management of community sites using spectral clustering and folksonomy

Community question answering (CQA) sites are the major platform for information sharing where posts are created by users as questions and answers. A large number of posts are created on a day-to-day basis, which raise the problem of information management of these sites. Multiple techniques are suggested in existing research for efficient management of CQA sites. Many of the existing techniques used the user ranking for managing the CQA sites but ignored the tagging data and user subject area. In this article, a user ranking method is derived using spectral clustering for posts management by considering the tagging data of CQA sites. Folksonomy is used to build relationship between tags, posts and users. The proposed method is developed in three stages. In first stage, the folksonomy relation is created and user similarity graph is built with the help of tag frequency-inverse post frequency and text similarity techniques. In the second stage, spectral clustering algorithm is applied on user similarity graph to group the similar users. Finally, in third stage, rank of users is identified from the clusters based on user’s information. The clustered users and rank of the users are generated as the output of the proposed algorithm that can provide a way of efficient information management. The experimental results show that the proposed user ranking algorithm outperforms the other considered ranking algorithms and can be helpful for information management of CQA sites. Some real-life applications of information management in CQA sites using the proposed work are also demonstrated in this article.