Deep & Cross Network and its derivative models have become an important paradigm for
click-through rate (CTR) prediction due to their effective balance between computational …

Click-through rate (CTR) prediction is an essential component of industrial multimedia
recommendation, and the key to enhancing the accuracy of CTR prediction lies in the …

In recommendation systems, new items are continuously introduced, initially lacking
interaction records but gradually accumulating them over time. Accurately predicting the …

The rapid development of online recruitment platforms has created unprecedented
opportunities for job seekers while concurrently posing the significant challenge of quickly …

Click-through rate (CTR) prediction is essential for targeted advertising systems. Although
there have been many studies on CTR prediction and forming some representative models …

Industrial recommender systems face the challenge of operating in non-stationary
environments, where data distribution shifts arise from evolving user behaviors over time. To …

Effective feature interaction modeling is critical for enhancing the accuracy of click-through
rate (CTR) prediction in industrial recommender systems. Most of the current deep CTR …

GPRec explicitly categorizes users into groups in a learnable manner and aligns them with
corresponding group embeddings. We design the dual group embedding space to offer a …

Data ecosystems (DE) are the future directions of data management and play a vital role in
unlocking the value of data. Service Recommender Systems (RS) are typical applications in …

Click-through rate (CTR) prediction is a crucial task in personalized information retrievals,
such as industrial recommender systems, online advertising, and web search. Most existing …