Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in Recommenders

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F24%3A00378729" target="_blank" >RIV/68407700:21240/24:00378729 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3656641" target="_blank" >https://doi.org/10.1145/3656641</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3656641" target="_blank" >10.1145/3656641</a>

Result language

angličtina

Original language name

Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in Recommenders

Original language description

Regionalization, also known as spatially constrained clustering, is an unsupervised machine learning technique used to identify and define spatially contiguous regions. In this work, we introduce a methodology to regionalize recommendation systems (RSs) based on a collaborative filtering approach. Two main challenges arise when performing regionalization based on users' preferences in RSs: (1) unstructured data, as interactions are often scarce and observed on a smaller scale; and (2) the difficulty of evaluation of the quality of the clustering results. To address these challenges, our methodology relies on inductive matrix completion (IMC), a fundamental approach to recover unknown entries of a rating matrix while utilizing region information to extract a region-based feature matrix. With this feature matrix, our method becomes adaptive and seamlessly integrates with various regionalization algorithms to create regionalization candidates. This enables us to derive more accurate recommendations that consider regionalized effects and discover interesting patterns in localized user behavior. We experimentally evaluate our model on synthetic datasets to demonstrate its efficacy in settings where our underlying assumptions are correct. Furthermore, we present a real-world case study illustrating the interpretable information the model can derive in terms of regionalized recommendation relevance.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

ACM Transactions on Spatial Algorithms and Systems

ISSN

2374-0353

e-ISSN

2374-0361

Volume of the periodical

10

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

23

Pages from-to

1-23

UT code for WoS article

001273116000002

EID of the result in the Scopus database

2-s2.0-85198019640