Personalized recommendation has been instrumental in many real applications. Despite the
great progress, the underlying multi-scenario characteristics (eg, users may behave …

Sequential recommendation, which aims to recommend next item that the user will likely
interact in a near future, has become essential in various Internet applications. Existing …

Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been
shown to enhance Click-Through Rate (CTR) prediction performance. However, online …

Travel recommendation aims to infer travel intentions of users by analyzing their historical
behaviors on Online Travel Agencies (OTAs). However, crucial keywords in clicked travel …

Deep neural network based recommendation systems have achieved great success as
information filtering techniques in recent years. However, since model training from scratch …

Impressions are a novel data source providing researchers and practitioners with more
details about user interactions and their context. In particular, an impression contain the …

Recommender system (RS) is widely applied in a multitude of scenarios to aid individuals
obtaining the information they require efficiently. At the same time, the prevalence of …

Estimating Click-Through Rate (CTR) is a vital yet challenging task in personalized product
search. However, existing CTR methods still struggle in the product search settings due to …

CTR (Click-Through Rate) prediction plays an essential role in online advertising systems.
Most existing works attempt to capture users' interests from sessions by assuming that …

The recommendation has been playing a key role in many industries, eg, e-commerce,
streaming media, social media, etc. Recently, a new recommendation scenario, called …