Tagging And Ranking Contents In Social Media

With the rapid growth of the social media data, helping users find valuable contents and organize them becomes a meaningful task. We study content tagging and ranking in this dissertation. Content tagging identifies content-related tags and helps users understand items. Web pages, short texts, images, videos and other types of content can be connected with each other by tags. Content ranking ranks items according to user preferences, time,space and other factors, which helps user find valuable information from the big data. The contributions of this dissertation are as follows.1. Tag recommendation in social tagging systems. In order to alleviate data sparsity, our model incorporates social network, tag semantic relatedness and item content similarity for personalized tag recommendation. We extends supervised random walk on heterogenous graphs. Node and edge weight is found by solving an optimization problem.The experimental results show that our method outperforms existing methods when making use of the same information. The recommendation precision can be further improved by incorporating new relations.2. Hashtag recommendation for short texts in microblogging systems. Hashtags in microblogging can be considered as an extension of tags from social tagging systems.First we discuss the characteristics of bursting hashtags, personal hashtags and general hashtags as well as content-based and user-based recommendation strategies. Then we propose a hashtag recommendation algorithm with the hybrid recommendation strategy to help users annotate items easily. Microblogging-specific symbols, user annotation preference, temporal effects of hashtags are considered. The experimental results show that our algorithm outperforms existing content-based methods and user-based methods.3. Personalized ranking for short texts in microblogging systems. Tweets are ranked in chronological order by default. However, users have to scan pages of tweets to find interesting contents. We study how to model personal preference according to the retweet history and rank tweets according to their interestingness. Content quality, authorship, the consistency of user preference and contents and user trust are considered in our model.The experimental results show that our algorithm outperforms existing methods. We also analyzed the importance of each component.4. Event tagging and ranking: We propose a real-time hashtag clustering algorithm to represent an event as a group of high-quality hashtags. Events are ranked according to their popularity, burstiness and localness to help users identify valuable information quickly. To explore events with different time and space granularity, we organize events with a data cube spanned by time and space. Clustering results are merged incrementally according to the spatial-temporal hierarchy. The experimental results show that our method can provide high-quality clusters and ranking results. Our methods scales well for large datasets.