Keerthi Nelaturu, Ying Qiao, Iluju Kiringa and TetHin Yeap, University of Ottawa, Canada

In this paper focus is on developing a hashtag recommendation system for an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. A user may invoke a recommendation procedure while writing the content. After being invoked, the recommendation procedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content. The proposed approach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content. LDA topic model is a well-developed data mining algorithm and generally effective in analysing text documents with different lengths. The topic model is used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden topics. The experiments for evaluating the recommendation approach were fed with the tweets published in Twitter. Hit-rate of recommendation is considered as an evaluation metricfor our experiments. Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags. Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently. Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.

Bestpeer, baton, Hashtag, topic model, hit-rate and peer-to-peer networks.