The Utility Problem of Web Content Popularity Prediction

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Moniz, N (Author) Other; Torgo, L (Author) FCUP

Conference proceedings International

Title: Proceedings of the 29th on Hypertext and Social Media, HT 2018, Baltimore, MD, USA, July 09-12, 2018

Pages: 82-86

29th ACM International Conference on Hypertext and Social Media, HT 2018

9 July 2018 through 12 July 2018

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00N-ZS9

DOI: 10.1145/3209542.3209573

Abstract (EN): The ability to generate and share content on social media platforms has changed the Internet. With the growing rate of content generation, efforts have been directed at making sense of such data. One of the most researched problem concerns predicting web content popularity. We argue that the evolution of state-of-the-art approaches has been optimized towards improving the predictability of average behaviour of data: items with low levels of popularity. We demonstrate this effect using a utility-based framework for evaluating numerical web content popularity prediction tasks, focusing on highly popular items. Additionally, it is demonstrated that gains in predictive and ranking ability of such type of cases can be obtained via naïve approaches, based on strategies to tackle imbalanced domains learning tasks. © 2018 Association for Computing Machinery.

Language: English

Type (Professor's evaluation): Scientific