[HTML][HTML] A systematic review of the literature on deep learning approaches for cross-domain recommender systems
The increase in online information and the expanding diversity of user preferences require
developing improved recommender systems. Cross-domain recommender systems (CDRS) …
developing improved recommender systems. Cross-domain recommender systems (CDRS) …
Rethinking cross-domain sequential recommendation under open-world assumptions
Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …
Multi-domain recommendation with embedding disentangling and domain alignment
Multi-domain recommendation (MDR) aims to provide recommendations for different
domains (eg, types of products) with overlapping users/items and is common for platforms …
domains (eg, types of products) with overlapping users/items and is common for platforms …
Towards open-world cross-domain sequential recommendation: A model-agnostic contrastive denoising approach
Cross-domain sequential recommendation (CDSR) aims to address the data spCH).
Recently, some SR approaches have utilized auxiliary behaviors to complement the …
Recently, some SR approaches have utilized auxiliary behaviors to complement the …
An Active Masked Attention Framework for Many-to-Many Cross-Domain Recommendations
Cross-Domain Recommendation (CDR) has been proposed to improve the
recommendation accuracy in the target domain (the sparser dataset) by benefiting from the …
recommendation accuracy in the target domain (the sparser dataset) by benefiting from the …
A cross-domain recommendation model by unified modelling high-order information and rating information
M Yi, M Liu, C Feng, W Deng - Journal of Information Science, 2023 - journals.sagepub.com
Cross-domain recommendation models are proposed to enrich the knowledge in the target
domain by taking advantage of the data in the auxiliary domain to mitigate sparsity and cold …
domain by taking advantage of the data in the auxiliary domain to mitigate sparsity and cold …
Federated Prototype-based Contrastive Learning for Privacy-Preserving Cross-domain Recommendation
L Wang, Q Zhang, L Sang, Q Wu, M Xu - arXiv preprint arXiv:2409.03294, 2024 - arxiv.org
Cross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse
domains by transferring knowledge from data-rich domains. However, existing CDR …
domains by transferring knowledge from data-rich domains. However, existing CDR …
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation
Cross-Domain Sequential Recommendation (CDSR) methods aim to address the data
sparsity and cold-start problems present in Single-Domain Sequential Recommendation …
sparsity and cold-start problems present in Single-Domain Sequential Recommendation …
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Random
In most practical applications such as recommendation systems, display advertising, and so
forth, the collected data often contains missing values and those missing values are …
forth, the collected data often contains missing values and those missing values are …
Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation
Cross-domain recommendation (CDR) aims to address the data-sparsity problem by
transferring knowledge across domains. Existing CDR methods generally assume that the …
transferring knowledge across domains. Existing CDR methods generally assume that the …