Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network
Abstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’...
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doaj-3abf61831ccc4687acdc4204e9db14ac2021-04-02T13:39:06ZengSpringerOpenData Science and Engineering2364-11852364-15412019-11-0151274710.1007/s41019-019-00113-0Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social NetworkJun He0Hongyan Liu1Yiqing Zheng2Shu Tang3Wei He4Xiaoyong Du5Key Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of ChinaDepartment of Management Science and Engineering, Tsinghua UniversityKey Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of ChinaKey Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of ChinaKey Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of ChinaKey Laboratory of Data Engineering and Knowledge Engineering, MOE, Renmin University of ChinaAbstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results.https://doi.org/10.1007/s41019-019-00113-0Topic modelLDALabeled LDASocial networkSocial taggingRandom walk |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jun He Hongyan Liu Yiqing Zheng Shu Tang Wei He Xiaoyong Du |
spellingShingle |
Jun He Hongyan Liu Yiqing Zheng Shu Tang Wei He Xiaoyong Du Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network Data Science and Engineering Topic model LDA Labeled LDA Social network Social tagging Random walk |
author_facet |
Jun He Hongyan Liu Yiqing Zheng Shu Tang Wei He Xiaoyong Du |
author_sort |
Jun He |
title |
Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network |
title_short |
Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network |
title_full |
Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network |
title_fullStr |
Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network |
title_full_unstemmed |
Bi-Labeled LDA: Inferring Interest Tags for Non-famous Users in Social Network |
title_sort |
bi-labeled lda: inferring interest tags for non-famous users in social network |
publisher |
SpringerOpen |
series |
Data Science and Engineering |
issn |
2364-1185 2364-1541 |
publishDate |
2019-11-01 |
description |
Abstract User tags in social network are valuable information for many applications such as Web search, recommender systems and online advertising. Thus, extracting high quality tags to capture user interest has attracted many researchers’ study in recent years. Most previous studies inferred users’ interest based on text posted in social network. In some cases, ordinary users usually only publish a small number of text posts and text information is not related to their interest very much. Compared with famous user, it is more challenging to find non-famous (ordinary) user’s interest. In this paper, we propose a probabilistic topic model, Bi-Labeled LDA, to automatically find interest tags for non-famous users in social network such as Twitter. Instead of extracting tags from text posts, tags of non-famous users are inferred from interest topics of famous users. With the proposed model, the formulation of social relationship between non-famous users and famous user is simulated and interest tags of famous users are exploited to supervise the training of the model and to make use of latent relation among famous users. Furthermore, the influence of popularity of famous user and popular tags are considered, and tags of non-famous users are ranked based on random walk model. Experiments were conducted on Twitter real datasets. Comparison with state-of-the-art methods shows that our method is more superior in terms of both ranking and quality of the tagging results. |
topic |
Topic model LDA Labeled LDA Social network Social tagging Random walk |
url |
https://doi.org/10.1007/s41019-019-00113-0 |
work_keys_str_mv |
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