Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …

Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble
problem when users suffer the familiar, repeated, and even predictable recommendations …

Recommender systems rely on user-item interactions to learn Collaborative Filtering (CF)
signals and easily under-recommend the cold-start items without historical interactions. To …

Recommending out-of-vocabulary (OOV) items is a challenging problem since the in-
vocabulary (IV) items have well-trained behavioral embeddings but the OOV items only have …

User preferences behind users' decision-making processes are highly diverse and may
range from lower-level concepts with more specific intentions and higher-level concepts with …

For many recommender systems, the primary data source is a historical record of user clicks.
The associated click matrix is often very sparse, as the number of users× products can be far …

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 …

Recommender systems are widely applied on web. For example, online advertising systems
rely on recommender systems to accurately estimate the value of display opportunities …

Achieving fairness among different individuals or groups is an essential task for industrial
recommender systems. Due to the group's personalized selection tendencies and the non …

The cold-start problem of conversion rate prediction is a common challenge in online
advertising systems. To alleviate this problem, a large number of methods either use content …