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.
|Number of pages||7|
|Journal||IJCAI International Joint Conference on Artificial Intelligence|
|Publication status||Published - 2016|
|Event||25th International Joint Conference on Artificial Intelligence, IJCAI 2016 - New York, United States|
Duration: Jul 9 2016 → Jul 15 2016
All Science Journal Classification (ASJC) codes
- Artificial Intelligence