research-article

Novel Positive Multi-Layer Graph Based Method for Collaborative Filtering Recommender Systems

Authors:

Bushra Alhijawi,

Ghazi AL-NaymatAuthors Info & Claims

Journal of Computer Science and Technology, Volume 37, Issue 4

Pages 975 - 990

https://doi.org/10.1007/s11390-021-0420-2

Published: 01 July 2022 Publication History

Abstract

Recommender systems play an increasingly important role in a wide variety of applications to help users find favorite products. Collaborative filtering has remarkable success in terms of accuracy and becomes one of the most popular recommendation methods. However, these methods have shown unpretentious performance in terms of novelty, diversity, and coverage. We propose a novel graph-based collaborative filtering method, namely Positive Multi-Layer Graph-Based Recommender System (PMLG-RS). PMLG-RS involves a positive multi-layer graph and a path search algorithm to generate recommendations. The positive multi-layer graph consists of two connected layers: the user and item layers. PMLG-RS requires developing a new path search method that finds the shortest path with the highest cost from a source node to every other node. A set of experiments are conducted to compare the PMLG-RS with well-known recommendation methods based on three benchmark datasets, MovieLens-100K, MovieLens-Last, and Film Trust. The results demonstrate the superiority of PMLG-RS and its high capability in making relevant, novel, and diverse recommendations for users.

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Cited By

Sheng ZZhang WTao YCui B(2024)OUTRE: An OUT-of-Core De-REdundancy GNN Training Framework for Massive Graphs within A Single MachineProceedings of the VLDB Endowment10.14778/3681954.368197617:11(2960-2973)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681976

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Information

Published In

cover image Journal of Computer Science and Technology

Journal of Computer Science and Technology Volume 37, Issue 4

Jul 2022

262 pages

ISSN:1000-9000

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© Institute of Computing Technology, Chinese Academy of Sciences 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 July 2022

Accepted: 29 April 2021

Received: 04 March 2020

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Cited By

Sheng ZZhang WTao YCui B(2024)OUTRE: An OUT-of-Core De-REdundancy GNN Training Framework for Massive Graphs within A Single MachineProceedings of the VLDB Endowment10.14778/3681954.368197617:11(2960-2973)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681976

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