In this paper, we propose a novel ranking framework for collaborative filtering with the
overall aim of learning user preferences over items by minimizing a pairwise ranking loss …

摘要近年来, 随着互联网及智能移动设备的发展和普及, 丰富了广告的推送方式和投放平台.
但是传统的广告推送无法满足用户对个性化广告的需求, 导致用户对广告产生抵触情绪 …

Cardinality estimation plays an important role in processing big data. We consider the
challenging problem of computing millions or more distinct count aggregations in a single …

Estimating the number of distinct values (NDV) in a column is useful for many tasks in
database systems, such as columnstore compression and data profiling. In this work, we …

In this paper, we present a novel and publicly available dataset for online recommendation
provided by Purch1. The dataset records the clicks generated by users of one of Purch's …

Many real-world data are naturally represented as a sparse reorderable matrix, whose rows
and columns can be arbitrarily ordered (eg, the adjacency matrix of a bipartite graph) …

The problem of estimating data properties using sampling frequency histograms has
attracted extensive interest in the area of databases. The properties include the number of …

Embedding is a crucial step for deep neural networks. Datasets, from different applications,
with different structures, can all be processed through an embedding layer and transformed …

Large, distributed data streams are now ubiquitous. High-accuracy sketches with low
memory overhead have become the de facto method for analyzing this data. For instance, if …

We introduce the notion of reverse-safe data structures. These are data structures that
prevent the reconstruction of the data they encode (ie, they cannot be easily reversed). A …