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Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users

Published: 03 November 2019 Publication History

Abstract

Providing accurate recommendations to newly joined users (or potential users, so-called cold-start users) has remained a challenging yet important problem in recommender systems. To infer the preferences of such cold-start users based on their preferences observed in other domains, several cross-domain recommendation (CDR) methods have been studied. The state-of-the-art Embedding and Mapping approach for CDR (EMCDR) aims to infer the latent vectors of cold-start users by supervised mapping from the latent space of another domain. In this paper, we propose a novel CDR framework based on semi-supervised mapping, called SSCDR, which effectively learns the cross-domain relationship even in the case that only a few number of labeled data is available. To this end, it first learns the latent vectors of users and items for each domain so that their interactions are represented by the distances, then trains a cross-domain mapping function to encode such distance information by exploiting both overlapping users as labeled data and all the items as unlabeled data. In addition, SSCDR adopts an effective inference technique that predicts the latent vectors of cold-start users by aggregating their neighborhood information. Our extensive experiments on different CDR scenarios show that SSCDR outperforms the state-of-the-art methods in terms of CDR accuracy, particularly in the realistic settings that a small portion of users overlap between two domains.

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
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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Published: 03 November 2019

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Author Tags

  1. collaborative filtering
  2. cross-domain recommendation
  3. metric learning
  4. neighborhood inference
  5. semi-supervised learning

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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  • (2025)MIMNet: Multi-interest Meta Network with Multi-granularity Target-guided Attention for cross-domain recommendationNeurocomputing10.1016/j.neucom.2024.129208620(129208)Online publication date: Mar-2025
  • (2025)Cross-domain recommendation via knowledge distillationKnowledge-Based Systems10.1016/j.knosys.2025.113112311(113112)Online publication date: Feb-2025
  • (2025)TJMN: Target-enhanced joint meta network with contrastive learning for cross-domain recommendationKnowledge-Based Systems10.1016/j.knosys.2024.112919310(112919)Online publication date: Feb-2025
  • (2025)Deep User Rating Pattern Mining and Fusion Inference Method for Cross-Domain RecommendationExpert Systems with Applications10.1016/j.eswa.2024.126374269(126374)Online publication date: Apr-2025
  • (2024)Reducing item discrepancy via differentially private robust embedding alignment for privacy-preserving cross domain recommendationProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693387(32455-32470)Online publication date: 21-Jul-2024
  • (2024)Check-In Heterogeneous Hypergraph and Personalized Preference Transfers for Cross-City POI Recommendation MethodElectronics10.3390/electronics1324495413:24(4954)Online publication date: 16-Dec-2024
  • (2024)Enhancing dual-target cross-domain recommendation with federated privacy-preserving learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/238(2153-2161)Online publication date: 3-Aug-2024
  • (2024)Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender SystemsACM Transactions on Recommender Systems10.1145/3649443Online publication date: 23-Feb-2024
  • (2024)Cross-Domain Latent Factors Sharing via Implicit Matrix FactorizationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688143(309-317)Online publication date: 8-Oct-2024
  • (2024)Instructing and Prompting Large Language Models for Explainable Cross-domain RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688137(298-308)Online publication date: 8-Oct-2024
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