Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | IJCAI | Feature selection,Computer science,Popularity,Artificial intelligence,Classifier (linguistics),Web service,Machine learning |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuya Konishi | 1 | 7 | 1.78 |
| Tomoharu Iwata | 2 | 824 | 65.87 |
| Hayashi, Kohei | 3 | 159 | 15.31 |
| Ken-ichi Kawarabayashi | 4 | 1731 | 149.16 |