TAGme: A Topical Folksonomy Based Collaborative Filtering for Tag Recommendation in Community Sites

Community Question Answering1 (CQA) sites allow users to share and exchange the knowledge on various fields. In recent years the CQA sites gain the huge popularity on the web. Searching the information is a very difficult task in CQA sites which is solved with the help of tags, but users assign the tags according to their knowledge. The CQA sites maintain metadata related to the posts, user and tags which can be utilized efficiently for recommending the tags. In this paper, a new algorithm, namely, TAGme, is proposed for tag recommendation in CQA sites. The proposed TAGme algorithm uses the concept of topic modeling, folksonomy and collaborative filtering. The proposed algorithm consists of three major stages. In the first stage, topical folksonomies are developed using the CQA metadata user, tag, topics the posts. In the second stage, the generated topical folksonomy is used to construct a topic profile matrix and user-topic profile matrix. Finally, at the third stage, collaborative filtering is used for tag recommendation based on users' previous topical history. Comparison of proposed tag recommendation algorithm TAGme is carried with standard tag recommendation algorithms, namely, TF, TF-IDF, LDA, tag-LDA, RTM and MAT. The experimental results demonstrate that the proposed TAGme algorithm performs better in comparison to the other tag recommendation algorithm.

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