Recommender Systems (RS) have proven to be effective tools to help users overcome
information overload, and significant advances have been made in the field over the past …

Session-based recommendation plays a central role in a wide spectrum of online
applications, ranging from e-commerce to online advertising services. However, the majority …

Recommender Systems (RS) aim to provide personalized suggestions of items for users
against consumer over-choice. Although extensive research has been conducted to address …

Existing works on session-based recommendation have shown the advantage in enhancing
the prediction ability of recommendation with various deep learning techniques. However …

Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start
problem. Auxiliary information like texts and images has been leveraged to alleviate these …

Top-N recommendations have been studied extensively. Promising results have been
achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in …

Tourism crowdsourcing platforms accumulate and use large volumes of feedback data on
tourism-related services to provide personalized recommendations with high impact on …

Nowadays, high-volume and privacy-sensitive data are generated by mobile devices, which
are better to be preserved on devices and queried on demand. However, data analysts still …

Online incremental models for recommendation are nowadays pervasive in both the industry
and the academia. However, there is not yet a standard evaluation methodology for the …

The recommendation problem in the hotel industry introduces several interesting and
unique challenges leading to the insufficiency of classical approaches. Traveling is not a …