Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a to...Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.展开更多
Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are inte...Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommenda- tion considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue spe- cially to recommend personalized hashtags combining long- term and short-term user interest. We introduce three features to capture personal and temporal user interest： 1） hashtag textual information; 2） user behavior; and 3） time. We of- fer two recommendation models for comparison： a linear- combined model, and an enhanced session-based temporal graph （STG） model, Topic-STG, considering the features to learn user preferences and subsequently recommend person- alized hashtags. Experiments on two real tweet datasets illus- trate the effectiveness of the proposed models and algorithms.展开更多
基金This work was supported by the National High Technology Research and Development Program of China（No. 2010AA012505, 2011AA010702, 2012AA01A401 and 2012AA01A402）, Chinese National Science Foundation （No. 60933005, 91124002,61303265）, National Technology Support Foundation （No. 2012BAH38B04） and National 242 Foundation （No. 2011A010）
文摘Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.
文摘Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommenda- tion considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue spe- cially to recommend personalized hashtags combining long- term and short-term user interest. We introduce three features to capture personal and temporal user interest： 1） hashtag textual information; 2） user behavior; and 3） time. We of- fer two recommendation models for comparison： a linear- combined model, and an enhanced session-based temporal graph （STG） model, Topic-STG, considering the features to learn user preferences and subsequently recommend person- alized hashtags. Experiments on two real tweet datasets illus- trate the effectiveness of the proposed models and algorithms.
