node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching

04/18/2019
by   Di Jin, et al.
0

Identity stitching, the task of identifying and matching various online references (e.g., sessions over different devices and timespans) to the same user in real-world web services, is crucial for personalization and recommendations. However, traditional user stitching approaches, such as grouping or blocking, require quadratic pairwise comparisons between a massive number of user activities, thus posing both computational and storage challenges. Recent works, which are often application-specific, heuristically seek to reduce the amount of comparisons, but they suffer from low precision and recall. To solve the problem in an application-independent way, we take a heterogeneous network-based approach in which users (nodes) interact with content (e.g., sessions, websites), and may have attributes (e.g., location). We propose node2bits, an efficient framework that represents multi-dimensional features of node contexts with binary hashcodes. node2bits leverages feature-based temporal walks to encapsulate short- and long-term interactions between nodes in heterogeneous web networks, and adopts SimHash to obtain compact, binary representations and avoid the quadratic complexity for similarity search. Extensive experiments on large-scale real networks show that node2bits outperforms traditional techniques and existing works that generate real-valued embeddings by up to 5.16 taking only up to 1.56

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/14/2021

Relation-aware Heterogeneous Graph for User Profiling

User profiling has long been an important problem that investigates user...
07/12/2020

Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

Network embedding which assigns nodes in networks to lowdimensional repr...
01/31/2019

HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

Given the intractability of large scale HIN, network embedding which lea...
07/19/2020

A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning

Network Embedding has been widely studied to model and manage data in a ...
01/14/2019

Search Efficient Binary Network Embedding

Traditional network embedding primarily focuses on learning a dense vect...
11/26/2017

BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder

Network embedding aims at projecting the network data into a low-dimensi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.