We present Bit Diffusion: a simple and generic approach for generating discrete data with
continuous state and continuous time diffusion models. The main idea behind our approach …

Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …

Modern deep neural networks (DNNs) have greatly facilitated the development of sequential
recommender systems by achieving state-of-the-art recommendation performance on …

Modern deep learning-based recommendation systems exploit hundreds to thousands of
different categorical features, each with millions of different categories ranging from clicks to …

Natural language processing (NLP) models often require a massive number of parameters
for word embeddings, resulting in a large storage or memory footprint. Deploying neural …

On-device session-based recommendation systems have been achieving increasing
attention on account of the low energy/resource consumption and privacy protection while …

Deep recommender systems have achieved remarkable improvements in recent years.
Despite its superior ranking precision, the running efficiency and memory consumption turn …

Lightweight compressed models are prevalent in Approximate Nearest Neighbor Search
(ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval …

Embedding layers are commonly used to map discrete symbols into continuous embedding
vectors that reflect their semantic meanings. Despite their effectiveness, the number of …

How to design an efficient method to handle mixed-attribute data classification (MADC)
problems has become a hot topic in data mining and machine learning. Current MADC …