Name
Abstract | ||
|---|---|---|
We propose a recommendation method that considers the user's individual preference and influence from other users in social media. This method predicts the user's individual preference and influence from other users by applying the probability of divergence from random-selection based on a statistical hypothesis test as a form of modified content-based filtering. We evaluated the proposed method by focusing on the rate at which items that have recommended tags are contained among all items. The proposed method is shown to have higher accuracy than traditional content-based filtering. It is especially effective when some percentage of the items have recommendation tags. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2492517.2500283 | Advances in Social Networks Analysis and Mining |
Keywords | Field | DocType |
recommendation tag,individual preference,recommender system,statistical hypothesis test,social media,recommendation method,higher accuracy,recommender systems,statistical testing | Recommender system,Social media,Collaborative filtering,Information retrieval,Computer science,Filter (signal processing),Artificial intelligence,Interpersonal influence,Statistical hypothesis testing,Machine learning,Information filtering system | Conference |
Citations | PageRank | References |
1 | 0.37 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tae Sato | 1 | 14 | 2.12 |
| Masanori Fujita | 2 | 1 | 0.37 |
| Minoru Kobayashi | 3 | 350 | 95.89 |
| Koji Ito | 4 | 1 | 0.37 |