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 …

Multi-interest collaborative metric learning has recently emerged as an effective approach to
modeling the multifaceted interests of a user in recommender systems. However, two issues …

Click-Through Rate (CTR) prediction of intelligent marketing systems is of great importance,
in which feature interaction selection plays a key role. Most approaches model interactions …

Graph neural network (GNN)-based models have been extensively studied for
recommendations, as they can extract high-order collaborative signals accurately which is …

Traditional POI recommendation systems use a centralized data storage approach to train
models, posing significant risks of privacy breaches. Federated learning offers an effective …

Cold-start problem is one of the long-standing challenges in recommender systems,
focusing on accurately modeling new or interaction-limited users or items to provide better …

Community question answering aims to connect queries and answers based on users'
community behaviors, find the most relevant solutions for newly raised questions, and …

Recommender models aimed at mining users' behavioral patterns have raised great
attention as one of the essential applications in daily life. Recent work on graph neural …

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 …

Meta-learning has become a widely used method for the user cold-start problem in
recommendation systems, as it allows the model to learn from similar learning tasks and …