Temporal information retrieval has been a topic of great interest in recent years. Its purpose
is to improve the effectiveness of information retrieval methods by exploiting temporal …

Learning to rank for information retrieval has gained a lot of interest in the recent years but
there is a lack for large real-world datasets to benchmark algorithms. That led us to publicly …

Traditional search engines return ranked lists of search results. It is up to the user to scroll
this list, scan within different documents, and assemble information that fulfill his/her …

This chapter gives a general introduction to learning for ranking aggregation. Ranking
aggregation is aimed at combining multiple rankings into a single ranking, which is better …

Voice-controlled intelligent personal assistants, such as Cortana, Google Now, Siri and
Alexa, are increasingly becoming a part of users' daily lives, especially on mobile devices …

Recommender System (RS) is an important online application that affects billions of users
every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task …

In recent times, data is growing rapidly in every domain such as news, social media,
banking, education, etc. Due to the excessiveness of data, there is a need of automatic …

There is a rapid growth in the use of voice-controlled intelligent personal assistants on
mobile devices, such as Microsoft's Cortana, Google Now, and Apple's Siri. They …

Search engines play a crucial role in our daily lives. Relevance is the core problem of a
commercial search engine. It has attracted thousands of researchers from both academia …

Realtime web search refers to the retrieval of very fresh content which is in high demand. An
effective portal web search engine must support a variety of search needs, including …