Pith. sign in

REVIEW 7 cited by

Cross-Domain Recommendation: Challenges, Progress, and Prospects

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 7 Pith papers

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

  1. HORIZON: A Benchmark for In-the-wild User Behaviour Modeling

    cs.IR 2026-04 unverdicted novelty 7.0 of 10

    HORIZON creates a cross-domain, long-horizon user modeling benchmark from Amazon Reviews that tests generalization across time, domains, and unseen users, exposing gaps in sequential and LLM-based recommendation models.

  2. SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation

    cs.IR 2026-01 unverdicted novelty 7.0 of 10

    SemaCDR builds a unified semantic space with LLM-generated domain-agnostic features and adaptive fusion to improve cross-domain sequential recommendations over baselines.

  3. Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

    cs.IR 2026-05 unverdicted novelty 6.0 of 10

    SCALR generates synthetic cross-domain events to augment recommendation training data and reports statistically significant improvements in industrial A/B tests.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

Pith tools