Toggle Main Menu Toggle Search

Open Access padlockePrints

Relation-aware Blocking for Scalable Recommendation Systems

Lookup NU author(s): Dr Huizhi Liang

Downloads


Licence

This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by ACM, 2022.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

Recommender systems contain rich relation information. The multiple relations in a recommender system form a heterogeneous information network. How to efficiently find similar users and items based on hop-n relations in heterogeneous information networks is one significant challenge to develop scalable recommender systems in the era of big data. Hashing has been popularly used for dimensionality reduction and data size reduction. Current hashing techniques mainly focus on hashing for directly related (i.e. hop-1) features. This paper proposes to develop relation-aware hashing techniques to bridge this gap. The proposed approaches use locality sensitive hashing (LSH) and consider hop-n relations in an information network to construct user or item blocks. They help facilitate efficient neighborhood formation and recommendation making. The experiments conducted on a large-scale real-life dataset show that the proposed approaches are effective.


Publication metadata

Author(s): Liang H, Liu Z, Markchom T

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 31st ACM International Conference on Information & Knowledge Management (CIKM '22)

Year of Conference: 2022

Pages: 4214-4218

Online publication date: 17/10/2022

Acceptance date: 02/08/2022

Date deposited: 03/09/2022

Publisher: ACM

URL: https://doi.org/10.1145/3511808.3557682

DOI: 10.1145/3511808.3557682

ePrints DOI: 10.57711/88f2-ef69

Library holdings: Search Newcastle University Library for this item

ISBN: 9781450392365


Share