Session-based recommendation aims to predict intents of anonymous users based on their
limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence …

Sequential recommendation is dedicated to offering items of interest for users based on their
history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items …

The session-based recommendation (SBR) garners increasing attention due to its ability to
predict anonymous user intents within limited interactions. Emerging efforts incorporate …

Session-based recommendation aims to recommend the next item based on short-term
interactions. Traditional session-based recommendation methods assume that all interacted …

The accurate assessment of consumer preferences has become increasingly crucial for
effective business decision-making. However, merchants face significant challenges in …

Session-based recommendation (SBR) aims to predict the next item a user might click within
an ongoing session, without relying on user profiles or historical data. Modern approaches …

Textual personality detection aims to identify personality traits by analyzing user-generated
content. To achieve this effectively, it is essential to thoroughly examine user-generated …

Most current session-based recommendations model session sequences solely based on
the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors …

Session-based recommendation approaches have garnered substantial attention for their
ability to deliver tailored recommendations across diverse domains. However, balancing …

News recommendation emerges as a primary means for users to access content of interest
from the vast amount of news. The title clickbait extensively exists in news domain and …