Cross-Domain Recommendation: Challenges, Progress, and Prospects (2103.01696v1)

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.

• The paper presents a comprehensive review of cross-domain recommendation, detailing challenges and categorizing methods to enhance sparse domains.
• It systematically examines three primary transfer techniques—content-based, embedding-based, and rating pattern-based—to improve recommendation accuracy.
• The study forecasts future directions such as heterogeneous, sequential, and privacy-preserving approaches, highlighting key areas for continued research.

Cross-Domain Recommendation: Challenges, Progress, and Prospects

The paper "Cross-Domain Recommendation: Challenges, Progress, and Prospects" presents a comprehensive review of the field of cross-domain recommendation (CDR), addressing the persistent issue of data sparsity in recommender systems. CDR has been advocated as a solution by utilizing the richer data available in one domain to enhance the recommendation performance in another, sparser domain. The paper systematically categorizes existing CDR approaches, identifies prevailing challenges, reports on research progress, and discusses potential future directions.

Challenges in Cross-Domain Recommendation

The paper thoroughly delineates the challenges facing CDR, categorized into four types: single-target CDR, multi-domain recommendation, dual-target CDR, and multi-target CDR. These issues stem from domain definition ambiguities, optimizing user/item embeddings, feature mapping, and potentially negative transfer effects inherent in multi-target scenarios. Particularly, challenges such as learning accurate mapping relations, combining embeddings for dual targets, and avoiding negative transfer in multi-target settings underscore the complexities involved in achieving efficient CDR.

Progress in Research

The review categorizes CDR approaches into three primary techniques: content-based transfer, embedding-based transfer, and rating pattern-based transfer, each addressing different challenges presented by data sparsity across domains.

Content-Based Transfer: These strategies link domains by identifying similar content such as user attributes, tags, and browsing history. The paper lists various methodologies, including leveraging semantic similarities, tag-induced relations, and utilizing multi-type media fusion.

Embedding-Based Transfer: This approach involves learning user/item latent factors using collaborative filtering models and transferring these embeddings across domains. Techniques explored include multi-task learning, transfer learning, clustering, and utilizing deep neural networks.

Rating Pattern-Based Transfer: This category focuses on transferring learned rating patterns from one domain to another to improve recommendation accuracy without directly transferring raw embeddings or content.

Evaluative Summary of Datasets

Several datasets are instrumental in developing and testing CDR techniques. Prominent datasets such as Arnetminer, MovieLens + Netflix, Amazon, and Douban are extensively used for their representative domain characteristics and data richness, providing varied user-item interactions across domains.

Prospects for Future Research

The paper envisages significant avenues for future inquiry within CDR, notably:

Heterogeneous CDR: Addressing the challenge of transferring across domains with differing data types and richness, proposing novel methods to harness information from e-commerce and social media domains.

Sequential CDR: The integration of sequential dependencies within user interactions in CDR models to enhance recommendation systems by modeling sequences longitudinally.

Privacy-Preserving CDR: Developing methods that ensure user privacy while leveraging sensitive data across domains. Current approaches are nascent and need substantial evolution to accommodate complex CDR settings while maintaining data integrity.

Conclusion

The paper provides a crucial synthesis of CDR methodologies, establishing a framework from which researchers can identify gaps in the current literature and propose robust strategies that address both the technical and practical concerns inherent in multi-domain data environments. While considerable advances have been made, the paper emphasizes the ongoing need for innovation to tackle emerging challenges, particularly in heterogeneous data environments and safeguarding user privacy. This comprehensive survey serves as a valuable resource for future developments in the CDR field.