Name
Abstract | ||
|---|---|---|
Social annotations are valuable resources generated by users on the Web, which encode abundant information on user preferences for certain documents. Social annotation-based information retrieval has been studied in recent years for personalizing search results and fulfilling user information needs. However, since social annotations are complicated and associated with users, documents and tags simultaneously, it remains a great challenge to fully capture the potentially useful information for improving retrieval performance. To meet the challenge, we propose a novel method to integrate social annotations into topic models for personalized document retrieval. Our method first reconstructs candidate documents for a given query using social tags of documents to capture user preferences. The reconstructed documents are tailored to user preferences for achieving better performance. We then generalize the latent Dirichlet allocation-based topic models by considering the relationship among users, social tags and documents from social annotations. The modified topic model optimizes the distribution of latent topics of documents for different users to meet user information needs. Experimental results show that our method can significantly outperform the state-of-the-art baseline models for improving the performance of personalized retrieval. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s00500-019-03998-1 | Soft Computing |
Keywords | Field | DocType |
Social annotations, Document reconstruction, Topic models, Document retrieval | ENCODE,Latent Dirichlet allocation,Document reconstruction,Annotation,Information retrieval,Computer science,User information,Artificial intelligence,Document retrieval,Topic model,Social tags,Machine learning | Journal |
Volume | Issue | ISSN |
24 | 3 | 1432-7643 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Xu | 1 | 95 | 28.26 |
| Hongfei Lin | 2 | 768 | 122.52 |
| Yuan Lin | 3 | 0 | 1.35 |
| Yizhou Guan | 4 | 0 | 0.34 |