@article{
 title = {User Based Aggregation for Biterm Topic Model},
 type = {article},
 pages = {489-494},
 websites = {http://www.aclweb.org/anthology/P15-2080},
 id = {6c24495e-d33b-31c9-adef-f8cc4873f285},
 created = {2018-02-05T17:41:02.916Z},
 accessed = {2018-02-05},
 file_attached = {true},
 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T17:41:05.010Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user's preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several state-of-the-art baselines.},
 bibtype = {article},
 author = {Chen, Weizheng and Wang, Jinpeng and Zhang, Yan and Yan, Hongfei and Li, Xiaoming}
}

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