---
title: "Identifying Key Observers to Find Popular Information in Advance"
authors:
  - "Takuya Konishi"
  - "Tomoharu Iwata"
  - "Kohei Hayashi"
  - "Ken-ichi Kawarabayashi"
doi: ""
url: "https://ijcai.org/proceedings/2016/529"
pdf: "https://ijcai.org/proceedings/2016/0529.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "3761-3766"
track: ""
keywords:
---

# Identifying Key Observers to Find Popular Information in Advance

Takuya Konishi, Tomoharu Iwata, Kohei Hayashi, Ken-ichi Kawarabayashi

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 3761-3766.

## Abstract

Identifying soon-to-be-popular items in web services offers important benefits. We attempt to identify users who can find prospective popular items. Such visionary users are called observers. By adding observers to a favorite user list, they act to find popular items in advance. To identify efficient observers, we propose a feature selection based framework. This uses a classifier to predict item popularity, where the input features are a set of users who adopted an item before others. By training the classifier with sparse and non-negative constraints, observers are extracted as users whose parameters take a non-zero value. In experiments, we test our approach using real social bookmark datasets. The results demonstrate that our approach can find popular items in advance more effectively than baseline methods.