Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.05348 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916352560726016 |
|---|---|
| author | Pang, Junbiao Huang, Qingming |
| author_facet | Pang, Junbiao Huang, Qingming |
| contents | Organizing a few webpages from social media websites into popular topics is one of the key steps to understand trends on web. Discovering popular topics from web faces a sea of noise webpages which never evolve into popular topics. In this paper, we discover that the similarity values between webpages in a popular topic contain the statistically similar features observed in Levy walks. Consequently, we present a simple, novel, yet very powerful Explore-Exploit (EE) approach to group topics by simulating Levy walks nature in the similarity space. The proposed EE-based topic clustering is an effective and effcient method which is a solid move towards handling a sea of noise webpages. Experiments on two public data sets demonstrate that our approach is not only comparable to the state-of-the-art methods in terms of effectiveness but also significantly outperforms the state-of-the-art methods in terms of efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05348 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space Pang, Junbiao Huang, Qingming Information Retrieval Organizing a few webpages from social media websites into popular topics is one of the key steps to understand trends on web. Discovering popular topics from web faces a sea of noise webpages which never evolve into popular topics. In this paper, we discover that the similarity values between webpages in a popular topic contain the statistically similar features observed in Levy walks. Consequently, we present a simple, novel, yet very powerful Explore-Exploit (EE) approach to group topics by simulating Levy walks nature in the similarity space. The proposed EE-based topic clustering is an effective and effcient method which is a solid move towards handling a sea of noise webpages. Experiments on two public data sets demonstrate that our approach is not only comparable to the state-of-the-art methods in terms of effectiveness but also significantly outperforms the state-of-the-art methods in terms of efficiency. |
| title | Towards Scalable Topic Detection on Web via Simulating Levy Walks Nature of Topics in Similarity Space |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2408.05348 |