Cross-Domain Recommendation: An Embedding and Mapping Approach

By Tong Man, Huawei Shen, Xiaolong Jin, Xueqi Cheng. 2017

505 citations recorded by the source corpus.

Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing crossdomain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yongfeng Zhang
2. Xiangnan He
3. Wayne Xin Zhao
4. Yupeng Hou
5. Yong Li
6. Chen Gao
7. Hongyu Lu
8. Depeng Jin

In Information Systems

1. Xiangnan He
2. Yongfeng Zhang
3. Wayne Xin Zhao
4. Yupeng Hou
5. Yong Li
6. Chen Gao
7. Hongyu Lu
8. Depeng Jin

In Management Science and Operations Research

1. Wayne Xin Zhao
2. R. Liu
3. Yongfeng Zhang
4. Jun Zhou
5. Yan Wang
6. Ruobing Xie
7. Julian McAuley
8. Yong Li

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “As an increasing number of users interact with multimodal information across multiple domains, cross-domain recommendation (CDR) has been demonstrated to be a promising method to alleviate cold start and data sparsity problems [50, 52, 70, 79, 124, 132, 238], as illustrated in Figure 7.”
• Cross-domain Recommendation Without Sharing User-relevant Data (2019, unknown): “With a similar setting, Man et al. [23] proposed a neural method which employs multi-layer perceptron to adapt user and item embeddings between two domains.”
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, background): “Deep neural networks [Jaradat, 2017] Ratings Textual input relations [Man et al., 2017] Ratings Linear matrix translation & MLP [Zhu et al.”
• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “Solutions to this problem either learn to model content features [14, 27, 40] so that inference can be made without interaction records or learn to transfer representations from auxiliary domains [39, 52, 55, 68, 78].”
• A Deep Framework for Cross-Domain and Cross-System Recommendations (2018, methodology): “…in the target domain or system with the corresponding latent factors in the source domain or system [Zhao et al. , 2017] ( Category 1 ), or map the latent factors of common users/items in the source domain or system to fit those in the target domain or system [Man et al. , 2017] ( Category 2 ).”
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, background): “We would like to take a more focused study on how they improve the recommendation performance of LLMs. Instruction tuning improves the ranking abilities of LLMs. Existing works show that instruction tuning significantly improves the generalization abilities of LLMs on unseen tasks [27, 32].”
• A General Cross-Domain Recommendation Framework via Bayesian Neural Network (2018, background): “One of the representative work is EMCDR [2] which captures the nonlinear mapping function between source domain and target domain with a multi-layer perceptron.”