short-paper

Recommendation as link prediction: a graph kernel-based machine learning approach

Authors:

Hsinchun ChenAuthors Info & Claims

JCDL '09: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries

Pages 213 - 216

https://doi.org/10.1145/1555400.1555433

Published: 15 June 2009 Publication History

Abstract

Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.

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Cited By

Kasuga AYonetani RSerra ESpezzano F(2024)CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign AssessmentProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679894(3817-3821)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679894
Talawar MAshoka DNagaraja R(2024)A Deep Learning-Based Reliable Link Prediction Model for Achieving Traffic-Aware Routing in Mobile Ad-hoc NetworksInternational Journal on Artificial Intelligence Tools10.1142/S021821302350072033:04Online publication date: 18-Jun-2024
https://doi.org/10.1142/S0218213023500720
Nunes IHeddes MVergés PAbraham DVeidenbaum ANicolau AGivargis TSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)DotHash: Estimating Set Similarity Metrics for Link Prediction and Document DeduplicationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599314(1758-1769)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599314
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Index Terms

Recommendation as link prediction: a graph kernel-based machine learning approach

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Published In

cover image ACM Conferences

JCDL '09: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries

June 2009

502 pages

ISBN:9781605583228

DOI:10.1145/1555400

General Chairs:
Fred Heath
University of Texas Libraries, USA
,
Mary Lynn Rice-Lively
University of Texas at Austin, USA
,
Program Chair:
Richard Furuta
Texas A&M University, USA

Copyright © 2009 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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Publication History

Published: 15 June 2009

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JCDL '09

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JCDL '09: Joint Conference on Digital Libraries

June 15 - 19, 2009

TX, Austin, USA

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Kasuga AYonetani RSerra ESpezzano F(2024)CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign AssessmentProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679894(3817-3821)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679894
Talawar MAshoka DNagaraja R(2024)A Deep Learning-Based Reliable Link Prediction Model for Achieving Traffic-Aware Routing in Mobile Ad-hoc NetworksInternational Journal on Artificial Intelligence Tools10.1142/S021821302350072033:04Online publication date: 18-Jun-2024
https://doi.org/10.1142/S0218213023500720
Nunes IHeddes MVergés PAbraham DVeidenbaum ANicolau AGivargis TSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)DotHash: Estimating Set Similarity Metrics for Link Prediction and Document DeduplicationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599314(1758-1769)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599314
Li JZhang CLi ZNie RHan PYang WLiao H(2022)GCMCDTI: Graph convolutional autoencoder framework for predicting drug–target interactions based on matrix completionJournal of Bioinformatics and Computational Biology10.1142/S021972002250023820:05Online publication date: 9-Nov-2022
https://doi.org/10.1142/S0219720022500238
Banerjee S(2022)A survey on mining and analysis of uncertain graphsKnowledge and Information Systems10.1007/s10115-022-01681-w64:7(1653-1689)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1007/s10115-022-01681-w
Gupta CJain YDe AChakrabarti SDemartini GZuccon GCulpepper JHuang ZTong H(2021)Integrating Transductive and Inductive Embeddings Improves Link Prediction AccuracyProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482125(3043-3047)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482125
Tan MChen WWang WLiu AZhao L(2021)Intention-oriented Hierarchical Bundle Recommendation with Preference Transfer2021 IEEE International Conference on Web Services (ICWS)10.1109/ICWS53863.2021.00026(107-116)Online publication date: Sep-2021
https://doi.org/10.1109/ICWS53863.2021.00026
Raut PKhandelwal HVyas G(2020)A Comparative Study of Classification Algorithms for Link Prediction2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)10.1109/ICIMIA48430.2020.9074840(479-483)Online publication date: Mar-2020
https://doi.org/10.1109/ICIMIA48430.2020.9074840
Atzmueller M(2019)Onto Model-based Anomalous Link Pattern Mining on Feature-Rich Social Interaction NetworksCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316707(1047-1050)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316707
Makarov IGerasimova O(2019)Predicting Collaborations in Co-authorship Network2019 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP)10.1109/SMAP.2019.8864887(1-6)Online publication date: Jun-2019
https://doi.org/10.1109/SMAP.2019.8864887
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