Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

The music domain is among the most important ones for adopting recommender systems
technology. In contrast to most other recommendation domains, which predominantly rely on …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Deep learning methods have led to substantial progress in various application fields of AI,
and in recent years a number of proposals were made to improve recommender systems …

Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …

Users may strive to formulate an adequate textual query for their information need. Search
engines assist the users by presenting query suggestions. To preserve the original search …

Session-based recommendation becomes a research hotspot for its ability to make
recommendations for anonymous users. However, existing session-based methods have …

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 …

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 …

The capability of extracting sequential patterns from the user-item interaction data is now
becoming a key feature of recommender systems. Though it is important to capture the …