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

TL;DR

This paper introduces node2bits, a novel framework for user identity stitching that creates compact, binary node representations in heterogeneous networks, significantly improving accuracy and efficiency over traditional methods.

Contribution

node2bits is the first application-independent method that uses binary hashcodes and feature-based temporal walks for efficient user stitching in large-scale web networks.

Findings

Outperforms traditional techniques by up to 5.16% in F1 score.

Uses only 1.56% of storage compared to real-valued embeddings.

Effectively captures multi-dimensional node features with binary representations.

Abstract

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% in F1 score on user stitching, while taking only up to 1.56% as much storage.