Topic models have been widely used for text analysis. Previous topic models have enjoyed great success in mining the latent topic structure of text documents. With many efforts made on endowing the resulting document-topic distributions with different motivations, however, none of these models have paid any attention on the resulting topic-word distributions.Since topic-word distribution also plays an important role in the modeling performance,topic models which emphasize only the resulting document-topic representations but pay less attention to the topic-term distributions are limited. In this paper, we propose the Orthogonalized Topic Model(OTM) which imposes an orthogonality constraint on the topic-term distributions. We also propose a novel model fitting algorithm based on the generalized Expectation-Maximization algorithm and the Newthon-Raphson method. Quantitative evaluation of text classification demonstrates that OTM outperforms other baseline models and indicates the important role played by topic orthogonalizing.

Twitter has rapidly grown to a popular social network in recent years and provides a large number of real-time messages for users. Tweets are presented in chronological order and users scan the followees' timelines to find what they are interested in. However, an information overload problem has troubled many users, especially those with many followees and thousands of tweets arriving every day. In this paper, we focus on recommending useful tweets that users are really interested in personally to reduce the users' effort to find useful information. Many kinds of information on Twitter are available for helping recommendation, including the user's own tweet history, retweet history and social relations between users. We propose a method of making tweet recommendations based on collaborative ranking to capture personal interests. It can also conveniently integrate the other useful contextual information. Our final method considers three major elements on Twitter: tweet topic level factors, user social relation factors and explicit features such as authority of the publisher and quality of the tweet. The experiments show that all the proposed elements are important and our method greatly outperforms several baseline methods.