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Top-N Recommendation with High-Dimensional Side Information via Locality Preserving Projection

Published: 07 August 2017 Publication History

Abstract

In this paper, we leverage high-dimensional side information to enhance top-N recommendations. To reduce the impact of the curse of high dimensionality, we incorporate a dimensionality reduction method, Locality Preserving Projection (LPP), into the recommendation model. A joint learning model is proposed to achieve the task of dimensionality reduction and recommendation simultaneously and iteratively. Specifically, item similarities generated by the recommendation model are used as the weights of the adjacency graph for LPP while the projections are used to bias the learning of item similarity. Employing LPP for recommendation not only preserves locality but also improves item similarity. Our experimental results illustrate that the proposed method is superior over state-of-the-art methods.

References

[1]
A. Elbadrawy and G. Karypis. User-specific feature-based similarity models for top-n recommendation of new items. ACM Trans. Intel. Syst. Tech., 6 (3): 33, 2015.
[2]
Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-Thieme. Learning attribute-to-feature mappings for cold-start recommendations. In 10th IEEE International Conference on Data Mining, ICDM 2010, Sydney, Australia, pages 176--185, 2010.
[3]
X. He and P. Niyogi. Locality preserving projections. In Advances in Neural Information Processing Systems 16: Annual Conference on Neural Information Processing Systems 2003, NIPS 2003, Vancouver and Whistler, British Columbia, Canada, pages 153--160, 2003.
[4]
J. Lu, G. Liang, J. Sun, and J. Bi. A sparse interactive model for matrix completion with side information. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, NIPS 2016, Barcelona, Spain, pages 4071--4079, 2016.
[5]
X. Ning and G. Karypis. SLIM: sparse linear methods for top-n recommender systems. In 11th IEEE International Conference on Data Mining, ICDM 2011, Vancouver, BC, Canada, pages 497--506, 2011.
[6]
X. Ning and G. Karypis. Sparse linear methods with side information for top-n recommendations. In Sixth ACM Conference on Recommender Systems, RecSys 2012, Dublin, Ireland, September 9-13, 2012, pages 155--162, 2012.
[7]
F. Ricci, L. Rokach, and B. Shapira, editors. Recommender Systems Handbook. Springer, 2015.
[8]
M. Saveski and A. Mantrach. Item cold-start recommendations: learning local collective embeddings. In Eighth ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA, pages 89--96, 2014.
[9]
F. Zhao, M. Xiao, and Y. Guo. Predictive collaborative filtering with side information. In Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, pages 2385--2391, 2016.

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    cover image ACM Conferences
    SIGIR '17: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
    August 2017
    1476 pages
    ISBN:9781450350228
    DOI:10.1145/3077136
    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 the author(s) 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: 07 August 2017

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    Author Tags

    1. dimensionality reduction
    2. high dimensionality
    3. side information
    4. top-n recommendation

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    • Short-paper

    Funding Sources

    • Elsevier
    • the Dutch national program COMMIT
    • Amsterdam Data Science
    • the European Community's Seventh Framework Programme (FP7/2007-2013)
    • the Microsoft Research Ph.D. program
    • the Netherlands Institute for Sound and Vision
    • Natural Science Foundation of Hunan
    • Yandex
    • Netherlands Organisation for Scientific Research
    • National Natural Science Foundation of China
    • Ahold Delhaize
    • the Bloomberg Research Grant program

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    SIGIR '17
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    SIGIR '17 Paper Acceptance Rate 78 of 362 submissions, 22%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

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    • (2025)Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural NetworkNeural Networks10.1016/j.neunet.2024.107071184(107071)Online publication date: Apr-2025
    • (2023)The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix FactorizationApplied Sciences10.3390/app13211202713:21(12027)Online publication date: 4-Nov-2023
    • (2022)Bayesian feature interaction selection for factorization machinesArtificial Intelligence10.1016/j.artint.2021.103589302:COnline publication date: 1-Jan-2022
    • (2021)Integrating Stacked Sparse Auto-Encoder Into Matrix Factorization for Rating PredictionIEEE Access10.1109/ACCESS.2021.30532919(17641-17648)Online publication date: 2021
    • (2021)Software defect prediction based on enhanced metaheuristic feature selection optimization and a hybrid deep neural networkJournal of Systems and Software10.1016/j.jss.2021.111026(111026)Online publication date: Jun-2021
    • (2020)Block-Aware Item Similarity Models for Top-N RecommendationACM Transactions on Information Systems10.1145/341175438:4(1-26)Online publication date: 10-Sep-2020
    • (2020)Closed-Form Models for Collaborative Filtering with Side-InformationProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3418480(651-656)Online publication date: 22-Sep-2020
    • (2020)An Enhanced Neural Network Approach to Person-Job Fit in Talent RecruitmentACM Transactions on Information Systems10.1145/337692738:2(1-33)Online publication date: 11-Feb-2020
    • (2020)Feature-Aware Attentive Variational Auto-Encoder for Top-N Recommendation2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00019(53-58)Online publication date: Nov-2020
    • (2020)Item Cold-Start Recommendation with Personalized Feature SelectionJournal of Computer Science and Technology10.1007/s11390-020-9864-z35:5(1217-1230)Online publication date: 30-Sep-2020
    • Show More Cited By

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