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Cross-Domain Recommendation: Challenges, Progress, and Prospects

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arxiv 2103.01696 v1 pith:WAUBBCBH submitted 2021-03-02 cs.IR cs.AIcs.LG

Feng Zhu , Yan Wang , Chaochao Chen , Jun Zhou , Longfei Li , Guanfeng Liu This is my paper
classification cs.IRcs.AIcs.LG
keywords approachesrecommendationchallengesexistingprogressresearchbeencross-domain
verification ladder T0 review T1 audit T2 compute T3 formal
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To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and future directions. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, multi-domain recommendation, dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising research directions in CDR.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multi-TAP: Multi-criteria Target Adaptive Persona Modeling for Cross-Domain Recommendation

    cs.HC 2026-03 conditional novelty 6.0 of 10

    Modeling intra-domain preference heterogeneity with multi-criteria LLM personas and target-adaptive doppelganger transfer beats prior CDR methods on Amazon domain pairs.

  2. Evaluating Recabilities of Foundation Models: A Multi-Domain, Multi-Dataset Benchmark

    cs.IR 2025-08 conditional novelty 6.0 of 10

    A large-scale benchmark finds that in-domain fine-tuning works best for foundation model recommenders, while cross-dataset and multi-domain training help in new scenarios.

  3. Revisiting Self-attention for Cross-domain Sequential Recommendation

    cs.IR 2025-05 conditional novelty 6.0 of 10

    AutoCDSR improves cross-domain sequential recommendation by adding a Pareto-optimized penalty on cross-domain attention scores to the standard recommendation loss.

  4. Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation

    cs.IR 2025-07 reject novelty 5.0 of 10

    A 200-person study of music-driven cross-domain recommendation for art therapy shows music-based and visual-based engines perform equally, contradicting the paper's 'outperforming' claim.

  5. RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation

    cs.IR 2025-09 conditional novelty 4.0 of 10

    RankGraph combines RGCN-style message passing, contrastive learning, and graph-token injection into foundation-model recommenders, reporting small online CTR and CVR gains.

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