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A Hybrid Conditional Variational Autoencoder Model for Personalised Top-n Recommendation

Published: 14 September 2020 Publication History

Abstract

The interactions of users with a recommendation system are in general sparse, leading to the well-known cold-start problem. Side information, such as age, occupation, genre and category, have been widely used to learn latent representations for users and items in order to address the sparsity of users' interactions. Conditional Variational Autoencoders (CVAEs) have recently been adapted for integrating side information as conditions to constrain the learned latent factors and to thereby generate personalised recommendations. However, the learning of effective latent representations that encapsulate both user (e.g. demographic information) and item side information (e.g. item categories) is still challenging. In this paper, we propose a new recommendation model, called Hybrid Conditional Variational Autoencoder (HCVAE) model, for personalised top-n recommendation, which effectively integrates both user and item side information to tackle the cold-start problem. Two CVAE-based methods -- using conditions on the learned latent factors, or conditions on the encoders and decoders -- are compared for integrating side information as conditions. Our HCVAE model leverages user and item side information as part of the optimisation objective to help the model construct more expressive latent representations and to better capture attributes of the users and items (such as demographic, category preferences) within the personalised item probability distributions. Thorough and extensive experiments conducted on both the MovieLens and Ta-feng datasets demonstrate that the HCVAE model conditioned on user category preferences with conditions on the learned latent factors can significantly outperform common existing top-n recommendation approaches such as MF-based and VAE/CVAE-based models.

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    cover image ACM Conferences
    ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval
    September 2020
    207 pages
    ISBN:9781450380676
    DOI:10.1145/3409256
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    Published: 14 September 2020

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    Author Tags

    1. conditional variational autoencoding
    2. recommendations

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    • (2024)Hierarchical Constrained Variational Autoencoder for interaction-sparse recommendationsInformation Processing & Management10.1016/j.ipm.2024.10364161:3(103641)Online publication date: May-2024
    • (2024)Disentangled-feature and composite-prior VAE on social recommendation for new usersExpert Systems with Applications10.1016/j.eswa.2024.123309247(123309)Online publication date: Aug-2024
    • (2023)Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature RecommendationApplied Sciences10.3390/app1302109313:2(1093)Online publication date: 13-Jan-2023
    • (2023)Graph Neural Pre-training for Recommendation with Side InformationACM Transactions on Information Systems10.1145/356895341:3(1-28)Online publication date: 7-Feb-2023
    • (2023)KFDBN: Kernelized Finetuned Deep Belief Network for recommendationMultimedia Tools and Applications10.1007/s11042-023-15208-083:8(23599-23634)Online publication date: 17-Aug-2023
    • (2023)Self-supervised variational autoencoder towards recommendation by nested contrastive learningApplied Intelligence10.1007/s10489-023-04488-653:15(18887-18897)Online publication date: 14-Feb-2023
    • (2022)Multi-Feature Extension via Semi-Autoencoder for Personalized RecommendationApplied Sciences10.3390/app12231240812:23(12408)Online publication date: 4-Dec-2022
    • (2022)Mutually-Regularized Dual Collaborative Variational Auto-encoder for Recommendation SystemsProceedings of the ACM Web Conference 202210.1145/3485447.3512110(2379-2387)Online publication date: 25-Apr-2022
    • (2022)MetaCAR: Cross-Domain Meta-Augmentation for Content-Aware RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3209005(1-14)Online publication date: 2022
    • (2022)Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00265(2942-2955)Online publication date: May-2022
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