scholarly journals Semantic Multimedia Retrieval using Lexical Query Expansion and Model-Based Reranking

2006 ◽  
Author(s):  
Alexander Haubold ◽  
Apostol Natsev ◽  
Milind Naphade
Keyword(s):  
Query Expansion ◽  
Multimedia Retrieval ◽  
Model Based ◽  
Lexical Query

Mobile Geographic Web Search Personalization with Language Model

2013 ◽  
Vol 303-306 ◽  
pp. 1420-1425
Author(s):  
Qiang Pu ◽  
Ahmed Lbath ◽  
Da Qing He
Keyword(s):  
Query Expansion ◽  
Web Search ◽  
Mixed Model ◽  
Topic Model ◽  
Language Model ◽  
Mobile Users ◽  
Information Need ◽  
User Interest ◽  
Modeling Framework ◽  
Model Based

Mobile personalized web search has been introduced for the purpose of distinguishing mobile user's personal different search interest. We first take the user's location information into account to do a geographic query expansion, then present an approach to personalizing web search for mobile users within language modeling framework. We estimate a user mixed model estimated according to both activated ontological topic model-based feedback and user interest model to re-rank the results from geographic query expansion. Experiments show that language model based re-ranking method is effective in presenting more relevant documents on the top retrieved results to mobile users. The main contribution of the improvements comes from the consideration of geographic information, ontological topic information and user interests together to find more relevant documents for satisfying their personal information need.

An Advanced User Intent Model Based On User Learning Process

2019 ◽  
Vol 34 (09) ◽  
pp. 2050024
Author(s):  
Bo Zhang ◽  
Xiaoxuan Qi ◽  
Xiaowei Han
Keyword(s):  
Learning Process ◽  
Query Expansion ◽  
Negative Impact ◽  
Feedback Information ◽  
User Intent ◽  
Model Based ◽  
Advanced User ◽  
User Learning

User intent analysis is a continuous research hotspot in the field of query expansion. However, the big amount of irrelevant feedbacks in search log has negatively impacted the precision of user intent model. By observing the log, it can be found that tentative click is a major source of irrelevant feedback. It is also observed that a kind of new feedback information can be extracted from the log to recognize the characteristics of tentative clicks. With this new feedback information, this paper proposes an advanced user intent model and applies it into query expansion. Experiment results show that the model can effectively decrease the negative impact of irrelevant feedbacks that belong to tentative clicks and increase the precision of query expansion, especially for those informational queries.

Query expansion model based on concept graph information retrieval

2009 ◽  
Vol 29 (2) ◽  
pp. 545-548 ◽  
Author(s):  
Rui CHEN ◽  
Lei ZHANG ◽  
Chun-jun LU ◽  
Li-ke MOU
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Model Based ◽  
Expansion Model
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