Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2014
Abstract
In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets - crawled from YouTube, Flickr and Last.fm - show that our method consistently outperforms competitive baselines in several evaluation tasks.
Keywords
Popularity Prediction, Item Ranking, Bipartite Graph Ranking, Comments Mining, BUIR
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Research Areas
Data Science and Engineering
Publication
SIGIR '14: Proceedings of 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Gold Coast, Australia, 2014 July 6-11
First Page
233
Last Page
242
ISBN
9781450322577
Identifier
10.1145/2600428.2609558
Publisher
ACM
City or Country
New York
Citation
HE, Xiangnan; Gao, Ming; KAN, Min-Yen; LIU, Yiqun; and SUGIYAMA, Kazunari.
Predicting the popularity of Web 2.0 items based on user comments. (2014). SIGIR '14: Proceedings of 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Gold Coast, Australia, 2014 July 6-11. 233-242.
Available at: https://ink.library.smu.edu.sg/sis_research/4228
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2600428.2609558
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons