Click-through rate (CTR) prediction is crucial in recommendation and online advertising
systems. Existing methods usually model user behaviors, while ignoring the informative …

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

Exploration-Exploitation (E& E) algorithms are commonly adopted to deal with the feedback-
loop issue in large-scale online recommender systems. Most of existing studies believe that …

The diversity of recommendation is equally crucial as accuracy in improving user
experience. Existing studies, eg, Determinantal Point Process (DPP) and Maximal Marginal …

The prediction of click-through conversion rates has always been an integral focus within e-
commerce platforms. This paper proposes a novel prediction model that leverages feature …

Vision-language models, pre-trained on web-scale datasets, have the potential to greatly
enhance the intelligence of web applications (eg, search engines, chatbots, and art tools) …

Click-Through Rate (CTR) prediction is a pivotal task in product and content
recommendation, where learning effective feature embeddings is of great significance …

Embedding-based deep recommendation models (EDRMs), which contain small dense
models and large embedding tables, are widely used in industry. Embedding …

Organic recommendation and advertising recommendation usually coexist on e-commerce
platforms. In this paper, we study the problem of utilizing data from organic recommendation …

It is noted that Click-Through-Rate (CTR) prediction plays an important part in
recommendation systems and online advertising. Over the past few years, numerous studies …