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GoRec: A Generative Cold-start Recommendation Framework

Published: 27 October 2023 Publication History

Abstract

Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals. In real scenarios, cold items appear in the test stage without any user interaction record. How to perform cold item recommendation is challenging as the training items and test items have different data distributions. These hybrid preference representations contained auxiliary collaborative signals, so current solutions designed alignment functions to transfer learned hybrid preference representations to cold items. Despite the effectiveness, we argue that they are still limited as these models relied heavily on the manually carefully designed alignment functions, which are easily influenced by the limited item records and noises in the training data.
To tackle the above limitations, we propose a Generative cold-start Recommendation (GoRec) framework for multimedia-based new item recommendation. Specifically, we design a Conditional Variational AutoEncoder~(CVAE) based method that first estimates the underlying distribution of each warm item conditioned on the multimedia content representation. Then, we propose a uniformity-enhanced optimization objective to ensure the latent space of CVAE is more distinguishable and informative. In the inference stage, a generative approach is designed to obtain warm-up new item representations from the latent distribution. Please note that GoRec is applicable to arbitrary recommendation backbones. Extensive experiments on three real datasets and various recommendation backbones verify the superiority of our proposed framework. The code is available at https://github.com/HaoyueBai98/GoRec.

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Cited By

View all
  • (2024)Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation18th ACM Conference on Recommender Systems10.1145/3640457.3688159(861-865)Online publication date: 8-Oct-2024
  • (2024)Content-based Graph Reconstruction for Cold-start Item RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657801(1263-1273)Online publication date: 10-Jul-2024
  • (2024)Teaching content recommendations in music appreciation courses via graph embedding learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02123-515:9(3847-3862)Online publication date: 16-May-2024

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  1. GoRec: A Generative Cold-start Recommendation Framework

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    cover image ACM Conferences
    MM '23: Proceedings of the 31st ACM International Conference on Multimedia
    October 2023
    9913 pages
    ISBN:9798400701085
    DOI:10.1145/3581783
    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: 27 October 2023

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    Author Tags

    1. cold start
    2. conditional variational auto-encoder
    3. recommender system

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    • Research-article

    Funding Sources

    • The National Key Research and Development Program of China
    • Major Project of Anhui Province
    • The National Natural Science Foundation of China

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    MM '23
    Sponsor:
    MM '23: The 31st ACM International Conference on Multimedia
    October 29 - November 3, 2023
    Ottawa ON, Canada

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2024)Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation18th ACM Conference on Recommender Systems10.1145/3640457.3688159(861-865)Online publication date: 8-Oct-2024
    • (2024)Content-based Graph Reconstruction for Cold-start Item RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657801(1263-1273)Online publication date: 10-Jul-2024
    • (2024)Teaching content recommendations in music appreciation courses via graph embedding learningInternational Journal of Machine Learning and Cybernetics10.1007/s13042-024-02123-515:9(3847-3862)Online publication date: 16-May-2024

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