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A Multiresolution Approach to Recommender Systems

Authors:

Gilbert Badaro,

Wassim El-Hajj,

Khaled Bashir ShabanAuthors Info & Claims

SNAKDD'14: Proceedings of the 8th Workshop on Social Network Mining and Analysis

Article No.: 9, Pages 1 - 5

https://doi.org/10.1145/2659480.2659501

Published: 24 August 2014 Publication History

Abstract

Recommender systems face performance challenges when dealing with sparse data. This paper addresses these challenges and proposes the use of Harmonic Analysis. The method provides a novel approach to the user-item matrix and extracts the interplay between users and items at multiple resolution levels. New affinity matrices are defined to measure similarities among users, among items, and across items and users. Furthermore, the similarities are assessed at multiple levels of granularity allowing individual and group level similarities. These affinity matrices thus produce multiresolution groupings of items and users, and in turn lead to higher accuracy in matching similar context for ratings, and more accurate prediction of new ratings. Evaluation results show superiority of the approach compared to state of the art solutions.

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Cited By

Badaro GHajj HHabash N(2020)A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNetACM Transactions on Asian and Low-Resource Language Information Processing10.1145/340485419:6(1-38)Online publication date: 13-Oct-2020
https://dl.acm.org/doi/10.1145/3404854
Zhang HSun YZhao MChow TWu Q(2020)Bridging User Interest to Item Content for Recommender Systems: An Optimization ModelIEEE Transactions on Cybernetics10.1109/TCYB.2019.290015950:10(4268-4280)Online publication date: Oct-2020
https://doi.org/10.1109/TCYB.2019.2900159
Badaro GBaly RHajj HEl-Hajj WShaban KHabash NAl-Sallab AHamdi A(2019)A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/329566218:3(1-52)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3295662
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A Multiresolution Approach to Recommender Systems

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cover image ACM Conferences

SNAKDD'14: Proceedings of the 8th Workshop on Social Network Mining and Analysis

August 2014

90 pages

ISBN:9781450331920

DOI:10.1145/2659480

Program Chair:
Feida Zhu

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

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Cited By

Badaro GHajj HHabash N(2020)A Link Prediction Approach for Accurately Mapping a Large-scale Arabic Lexical Resource to English WordNetACM Transactions on Asian and Low-Resource Language Information Processing10.1145/340485419:6(1-38)Online publication date: 13-Oct-2020
https://dl.acm.org/doi/10.1145/3404854
Zhang HSun YZhao MChow TWu Q(2020)Bridging User Interest to Item Content for Recommender Systems: An Optimization ModelIEEE Transactions on Cybernetics10.1109/TCYB.2019.290015950:10(4268-4280)Online publication date: Oct-2020
https://doi.org/10.1109/TCYB.2019.2900159
Badaro GBaly RHajj HEl-Hajj WShaban KHabash NAl-Sallab AHamdi A(2019)A Survey of Opinion Mining in ArabicACM Transactions on Asian and Low-Resource Language Information Processing10.1145/329566218:3(1-52)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3295662
Kaur SChalla RKumar NSolanki SSharma SKaur K(2017)Recommendation generation using typicality based collaborative filtering2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence10.1109/CONFLUENCE.2017.7943151(210-215)Online publication date: Jan-2017
https://doi.org/10.1109/CONFLUENCE.2017.7943151

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