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The Footprint of Factorization Models and Their Applications in Collaborative Filtering

Published: 29 November 2021 Publication History

Abstract

Factorization models have been successfully applied to the recommendation problems and have significant impact to both academia and industries in the field of Collaborative Filtering (CF). However, the intermediate data generated in factorization models’ decision making process (or training process, footprint) have been overlooked even though they may provide rich information to further improve recommendations. In this article, we introduce the concept of Convergence Pattern, which records how ratings are learned step-by-step in factorization models in the field of CF. We show that the concept of Convergence Patternexists in both the model perspective (e.g., classical Matrix Factorization (MF) and deep-learning factorization) and the training (learning) perspective (e.g., stochastic gradient descent (SGD), alternating least squares (ALS), and Markov Chain Monte Carlo (MCMC)). By utilizing the Convergence Pattern, we propose a prediction model to estimate the prediction reliability of missing ratings and then improve the quality of recommendations. Two applications have been investigated: (1) how to evaluate the reliability of predicted missing ratings and thus recommend those ratings with high reliability. (2) How to explore the estimated reliability to adjust the predicted ratings to further improve the predication accuracy. Extensive experiments have been conducted on several benchmark datasets on three recommendation tasks: decision-aware recommendation, rating predicted, and Top-N recommendation. The experiment results have verified the effectiveness of the proposed methods in various aspects.

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  1. The Footprint of Factorization Models and Their Applications in Collaborative Filtering

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    cover image ACM Transactions on Information Systems
    ACM Transactions on Information Systems  Volume 40, Issue 4
    October 2022
    812 pages
    ISSN:1046-8188
    EISSN:1558-2868
    DOI:10.1145/3501285
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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 November 2021
    Accepted: 01 September 2021
    Revised: 01 August 2021
    Received: 01 May 2020
    Published in TOIS Volume 40, Issue 4

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    Author Tags

    1. Recommendation system
    2. decision making process
    3. decision-aware recommendation
    4. recommendation self-rectification

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    • Australian Government through the Australian Research Council’s Discovery Projects funding scheme
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    • (2023)System Initiative Prediction for Multi-turn Conversational Information SeekingProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615070(1807-1817)Online publication date: 21-Oct-2023
    • (2023)RDPCFComputers and Security10.1016/j.cose.2023.103452134:COnline publication date: 1-Nov-2023
    • (2022)Hyperbolic Temporal Network EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323239835:11(11489-11502)Online publication date: 27-Dec-2022

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