Zhao, Wenyu and Zhou, Dong and Wu, Xuan and Lawless, Séamus and Liu, anxun (2017) An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems. EAI Endorsed Transactions on Collaborative Computing, 3 (12): e4. ISSN 2312-8623
|
Text
eai.9-10-2017.154549.pdf - Published Version Available under License Creative Commons Attribution No Derivatives. Download (1MB) | Preview |
Abstract
Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user’s past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user’s past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model utilizes recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted on two real-world collaborative social tagging datasets show that our proposed methods outperform state-of-the-art methods.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Personalized Social Search, Collaborative Social Tagging Systems, Latent Dirichlet Allocation, Neural Language Model, Query Expansion |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science QA75 Electronic computers. Computer science |
| Depositing User: | EAI Editor II. |
| Date Deposited: | 14 Sep 2020 11:04 |
| Last Modified: | 14 Sep 2020 11:04 |
| URI: | https://eprints.eudl.eu/id/eprint/250 |
Actions (login required)
 |
View Item |
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
