An Augmented User Model for Personalized Search in Collaborative Social 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. 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 qu ery 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.