Multi-behavior recommendation, which exploits auxiliary behaviors (eg, click and cart) to
help predict users' potential interactions on the target behavior (eg, buy), is regarded as an …

Traditional recommendation models that usually utilize only one type of user-item interaction
are faced with serious data sparsity or cold start issues. Multi-behavior recommendation …

Multi-behavior recommendation exploits multiple types of user-item interactions, such as
view and cart, to learn user preferences and has demonstrated to be an effective solution to …

Multi-types of user behavior data (eg, clicking, adding to cart, and purchasing) are recorded
in most real-world recommendation scenarios, which can help to learn users' multi-faceted …

Traditional recommendation usually focuses on utilizing only one target user behavior (eg,
purchase) but ignoring other auxiliary behaviors (eg, click, add to cart). Early efforts of multi …

Modern recommender systems often embed users and items into low-dimensional latent
representations, based on their observed interactions. In practical recommendation …

User purchasing prediction with multi-behavior information remains a challenging problem
for current recommendation systems. Various methods have been proposed to address it via …

Accurate user and item embedding learning is crucial for modern recommender systems.
However, most existing recommendation techniques have thus far focused on modeling …

Recommender systems have been demonstrated to be effective to meet user's personalized
interests for many online services (eg, E-commerce and online advertising platforms) …

A well-designed recommender system can accurately capture the attributes of users and
items, reflecting the unique preferences of individuals. Traditional recommendation …