Implicit user modeling for personalized search

By Xuehua Shen, Bin Tan, Chengxiang Zhai. International Conference on Information and Knowledge Management, 2005

578 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e7ab77773788242a365646b2a2b0aa7171be9a62

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In Information Systems

Citations in the corpus

Evaluation of contextual information retrieval effectiveness: overview of issues and research (2010, background): “In [92], the authors propose context-sensitive retrieval algorithms based on statistical language models.”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “bust to language variation across speakers (Yang and Eisenstein, 2017), though attempts at creating personalized language technologies exist in information retrieval (Shen et al., 2005), recommender systems (Basilico and Hofmann, 2004),”
Mining long-term search history to improve search accuracy (2006, methodology): “Previous work [5, 6] has proposed to use statistical language models for contextsensitive search.”
Query suggestion using hitting time (2008, background): “Speci.cally, if vj = QT (the original query) and vi is a URL, we assign new weights for Wij by L p(QT |URL,User) wij. j.=j wij = .”
Measuring personalization of web search (2013, background): “Personalized search has been extensively studied in the literature [29, 41–43, 48, 58, 61, 62].”
Improving one-class collaborative filtering by incorporating rich user information (2010, background): “Shen et al. [19] propose a decision-theoretic framework for optimizing search performance via user feedback.”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “The typical evaluation method used in existing personalized search research is to conduct user studies [23, 26, 6, 34, 28, 29, 19, 5, 31].”