The hidden Markov models are statistical models used in many real-world applications and
communities. The use of hidden Markov models has become predominant in the last …

In the last decade, the importance of analyzing information management systems logs has
grown, because log data constitute a relevant aspect in evaluating the quality of such …

The rapid growth of the Web in the past two decades has made it the largest publicly
accessible data source in the world. Web mining aims to discover useful information or …

Query auto completion is known to provide poor predictions of the user's query when her
input prefix is very short (eg, one or two characters). In this paper we show that context, such …

As there is an increasing trend of applications being expected to deal with big data that
usually do not fit in the main memory of a single machine, analyzing big data is a …

We present a context-aware neural ranking model to exploit users' on-task search activities
and enhance retrieval performance. In particular, a two-level hierarchical recurrent neural …

Understanding users' search intent expressed through their search queries is crucial to Web
search and online advertisement. Web query classification (QC) has been widely studied for …

Context information in search sessions has proven to be useful for capturing user search
intent. Existing studies explored user behavior sequences in sessions in different ways to …

The context of a search query often provides a search engine meaningful hints for
answering the current query better. Previous studies on context-aware search were either …

As queries submitted by users directly affect search experiences, how to organize queries
has always been a research focus in Web search studies. While search request becomes …