Nearest neighbor search is a fundamental and essential operation in applications from
many domains, such as databases, machine learning, multimedia, and computer vision …

This work develops a representation learning method for bipartite networks. While existing
works have developed various embedding methods for network data, they have primarily …

We present Tencent's ads recommendation system and examine the challenges and
practices of learning appropriate recommendation representations. Our study begins by …

In this paper, we study the problem of using deep neural networks (DNNs) for estimating the
cardinality of similarity queries. Intuitively, DNNs can capture the distribution of data points …

Neyshabur and Srebro proposed SIMPLE-LSH, which is the state-of-the-art hashing based
algorithm for maximum inner product search (MIPS). We found that the performance of …

Recent years have witnessed a widespread increase of interest in network representation
learning (NRL). By far most research efforts have focused on NRL for homogeneous …

The Eclat algorithm is one of the most widely used frequent itemset mining methods.
However, the inefficiency for calculating the intersection of itemsets makes it a time …

The Eclat algorithm is one of the most widely used frequent itemset mining methods. In the
normal Eclat algorithm and its variants, it is inefficient to calculate the intersection size of …

现有的网络表示学习算法主要是针对同质网络或异质网络设计的, 而忽略了在推荐系统,
搜索引擎和问答系统等领域出现的二分网络的特殊特征以及这类网络所携带着的非常丰富的 …

As a novel target in pharmacy, microRNA (miRNA) can regulate gene expression under
specific disease conditions to produce specific proteins. To date, many researchers …