Paper Info
Title
Exploring External Knowledge Base for Personalized Search in Collaborative Tagging Systems.
Abstract
Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services, especially those utilizing collaborative tagging data. 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 leverages 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 by utilizing real-world collaborative tagging data show that the methods proposed in the current paper outperform several non-personalized methods as well as existing personalized search methods by utilizing user models solely constructed from usage histories.
Year
DOI
Venue
2016
10.1007/978-3-319-59288-6_37
Lecture Notes of the Institute for Computer Sciences, Social Informatics, and Telecommunications Engineering
Keywords
Field
DocType
Personalized search,Collaborative tagging systems,Latent semantic models,Word embeddings,Query expansion
Latent Dirichlet allocation,Personalized search,Information retrieval,Query expansion,Computer science,User modeling,Knowledge base,Language model,Personalization,Generative model
Conference
Volume
ISSN
Citations 
201
1867-8211
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Dong Zhou134225.99
Xuan Wu2112.21
Wenyu Zhao3112.21
Séamus Lawless411130.18
Jianxun Liu564067.12