Text retrieval enables people to efficiently obtain the desired data from massive text data, so
has become one of the most popular services in information retrieval community. However …

Richard E. Rubin's book has served as the authoritative introductory text for generations of
library and information science practitioners, with each new edition taking in its stride the …

In information retrieval, query auto completion (QAC), also known as typeahead [Xiao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …

In this digital age, there is an abundance of online educational materials in public and
proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to …

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base
completion. Our model ConvKB advances state-of-the-art models by employing a …

Exploratory search is characterized by a user's uncertainty towards a complex information
seeking task. A user conducting such a search in an information retrieval (IR) system may …

In this article, we propose a Latent Dirichlet Allocation–(LDA) based topic-graph probabilistic
personalization model for Web search. This model represents a user graph in a latent topic …

Query auto-completion assists web search users in formulating queries with a few
keystrokes, helping them to avoid spelling mistakes and to produce clear query expressions …

Query auto completion (QAC) methods recommend queries to search engine users when
they start entering a query. Current QAC methods mostly rank query completions based on …

We address the problem of personalization in the context of eCommerce search.
Specifically, we develop personalization ranking features that use in-session context to …