Topic based automatic news recommendation using topic model and affinity propagation

Yonghui Wu; Yuxin Ding; Xiaolong Wang; Jun Xu

doi:10.1109/ICMLC.2010.5580881

Topic based automatic news recommendation using topic model and affinity propagation

Yonghui Wu^*
, Yuxin Ding
, Xiaolong Wang
, Jun Xu

^*Corresponding author for this work

Harbin Institute of Technology
Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

This paper presents a topic based web news recommendation method combining Affinity Propagation (AP) and Latent Dirichlet Allocation (IDA), which could automatically find the topics exist in the web pages and recommend the topic based news to Internet users. The topic distance is defined using IDA, which is used to generate the topic distance matrix. AP clustering is used to cluster the web page collections into different topic clusters. In order to prove the effect of combining AP and LDA, we sampled web page collections with different topics and web page collections. A series of experiments are implemented in these web pages. The comparison of clustering result of AP with information distance and AP with IDA are presented. The experiments show that our method combining AP and LDA is effective in topic based news recommendation system.

Original language	English
Title of host publication	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages	1299-1304
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLC.2010.5580881
State	Published - 2010
Externally published	Yes
Event	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China Duration: 11 Jul 2010 → 14 Jul 2010

Publication series

Name	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume	3

Conference

Conference	2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Country/Territory	China
City	Qingdao
Period	11/07/10 → 14/07/10

Keywords

Affinity propagation
Clustering
IDA
Recommendation system
Topic model

Access to Document

10.1109/ICMLC.2010.5580881

Cite this

Wu, Y., Ding, Y., Wang, X., & Xu, J. (2010). Topic based automatic news recommendation using topic model and affinity propagation. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (pp. 1299-1304). Article 5580881 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 3). https://doi.org/10.1109/ICMLC.2010.5580881

@inproceedings{bc33b305889943d888c0be11812b98e9,

title = "Topic based automatic news recommendation using topic model and affinity propagation",

abstract = "This paper presents a topic based web news recommendation method combining Affinity Propagation (AP) and Latent Dirichlet Allocation (IDA), which could automatically find the topics exist in the web pages and recommend the topic based news to Internet users. The topic distance is defined using IDA, which is used to generate the topic distance matrix. AP clustering is used to cluster the web page collections into different topic clusters. In order to prove the effect of combining AP and LDA, we sampled web page collections with different topics and web page collections. A series of experiments are implemented in these web pages. The comparison of clustering result of AP with information distance and AP with IDA are presented. The experiments show that our method combining AP and LDA is effective in topic based news recommendation system.",

keywords = "Affinity propagation, Clustering, IDA, Recommendation system, Topic model",

author = "Yonghui Wu and Yuxin Ding and Xiaolong Wang and Jun Xu",

year = "2010",

doi = "10.1109/ICMLC.2010.5580881",

language = "英语",

isbn = "9781424465262",

series = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

pages = "1299--1304",

booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

note = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 ; Conference date: 11-07-2010 Through 14-07-2010",

}

Wu, Y, Ding, Y, Wang, X & Xu, J 2010, Topic based automatic news recommendation using topic model and affinity propagation. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010., 5580881, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, vol. 3, pp. 1299-1304, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 11/07/10. https://doi.org/10.1109/ICMLC.2010.5580881

Topic based automatic news recommendation using topic model and affinity propagation. / Wu, Yonghui; Ding, Yuxin; Wang, Xiaolong et al.
2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 1299-1304 5580881 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Topic based automatic news recommendation using topic model and affinity propagation

AU - Wu, Yonghui

AU - Ding, Yuxin

AU - Wang, Xiaolong

AU - Xu, Jun

PY - 2010

Y1 - 2010

N2 - This paper presents a topic based web news recommendation method combining Affinity Propagation (AP) and Latent Dirichlet Allocation (IDA), which could automatically find the topics exist in the web pages and recommend the topic based news to Internet users. The topic distance is defined using IDA, which is used to generate the topic distance matrix. AP clustering is used to cluster the web page collections into different topic clusters. In order to prove the effect of combining AP and LDA, we sampled web page collections with different topics and web page collections. A series of experiments are implemented in these web pages. The comparison of clustering result of AP with information distance and AP with IDA are presented. The experiments show that our method combining AP and LDA is effective in topic based news recommendation system.

AB - This paper presents a topic based web news recommendation method combining Affinity Propagation (AP) and Latent Dirichlet Allocation (IDA), which could automatically find the topics exist in the web pages and recommend the topic based news to Internet users. The topic distance is defined using IDA, which is used to generate the topic distance matrix. AP clustering is used to cluster the web page collections into different topic clusters. In order to prove the effect of combining AP and LDA, we sampled web page collections with different topics and web page collections. A series of experiments are implemented in these web pages. The comparison of clustering result of AP with information distance and AP with IDA are presented. The experiments show that our method combining AP and LDA is effective in topic based news recommendation system.

KW - Affinity propagation

KW - Clustering

KW - IDA

KW - Recommendation system

KW - Topic model

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DO - 10.1109/ICMLC.2010.5580881

M3 - 会议稿件

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SN - 9781424465262

T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

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BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

Y2 - 11 July 2010 through 14 July 2010

ER -

Topic based automatic news recommendation using topic model and affinity propagation

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Keywords

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