THESIS
2014
xii, 130 pages : illustrations ; 30 cm
Abstract
In recent years, probabilistic topic modeling is gaining significant momentum
in both academia and industry. Many probabilistic topic models have been
proposed by researchers and have demonstrated good performance in real-word
applications such as text mining and location based services. However, with the
effectiveness of probabilistic topic modeling, applying it in web search scenarios
has rarely been studied in literature. In this thesis, we discuss how to adapt probabilistic
topic modeling to three major functionalities of contemporary search engines:
Web Search Query Log Analytics, Web Search Query Suggestion and Web
Search Query Processing.
In Web Search Query Log Analytics, we first discuss the temporal topic models,
which discover the dynamics of web search and profil...[
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In recent years, probabilistic topic modeling is gaining significant momentum
in both academia and industry. Many probabilistic topic models have been
proposed by researchers and have demonstrated good performance in real-word
applications such as text mining and location based services. However, with the
effectiveness of probabilistic topic modeling, applying it in web search scenarios
has rarely been studied in literature. In this thesis, we discuss how to adapt probabilistic
topic modeling to three major functionalities of contemporary search engines:
Web Search Query Log Analytics, Web Search Query Suggestion and Web
Search Query Processing.
In Web Search Query Log Analytics, we first discuss the temporal topic models,
which discover the dynamics of web search and profile each search engine
user. Then we discuss the spatial topic models that capture the latent relations
between query terms, URLs and geographical locations. In Web Search Query
Suggestion, we present our approach of utilizing probabilistic topic modeling to
capture search engine users’ preferences, with the focus of integrating both diversification
and personalization into the procedure of generating search query suggestion
lists. In Web Search Query Processing, we first discuss two paradigms of
incorporating probabilistic topic information into inverted index, then we present efficient query processing algorithms for top-k document retrieval with both TF-IDF
and probabilistic topic information. We further demonstrate how to apply
the proposed inverted indices and query processing algorithms to the scenario of
mobile application search. Finally, we discuss possible future work of applying
probabilistic topic modeling in web search scenarios.
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