Session 15: Cross-domain Recommendation

Date: Friday September 22, 2:00 PM – 3:20 PM (GMT+8)
Room: Hall 406CX
Session Chair: Marko Tkalcic
Parallel with: Session 16: Multimedia Recommendation

  • RESDREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender
    by Xiaoxin Ye (School of Computer Science and Engineering, UNSW), Yun Li (School of Computer Science and Engineering, UNSW) and Lina Yao (CSIRO Data61, School of Computer Science and Engineering UNSW).

    Cross-Domain Sequential Recommendation(CDSR) aims to generate accurate predictions for future interactions by leveraging users’ cross-domain historical interactions. One major challenge of CDSR is how to jointly learn the single- and cross-domain user preferences efficiently. To enhance the target domain’s performance, most existing solutions start by learning the single-domain user preferences within each domain and then transferring the acquired knowledge from the rich domain to the target domain. However, this approach ignores the inter-sequence item relationship and also limits the opportunities for target domain knowledge to enhance the rich domain performance. Moreover, it also ignores the information within the cross-domain sequence. Despite cross-domain sequences being generally noisy and hard to learn directly, they contain valuable user behavior patterns with great potential to enhance performance. Another key challenge of CDSR is data sparsity, which also exists in other recommendation system problems. In the real world, the data distribution of the recommendation system is highly skewed to the popular products, especially on the large-scale dataset with millions of users and items. One more challenge is the class imbalance problem, inherited by the Sequential Recommendation problem. Generally, each sample only has one positive and thousands of negative samples. To address the above problems together, an innovative Decoupled Representation via Extraction Attention Module (DREAM) is proposed for CDSR to simultaneously learn single- and cross-domain user preference via decoupled representations. A novel Supervised Contrastive Learning framework is introduced to model the inter-sequence relationship as well as address the data sparsity via data augmentations. DREAM also leverages Focal Loss to put more weight on misclassified samples to address the class-imbalance problem, with another uplift on the overall model performance. Extensive experiments had been conducted on two cross-domain recommendation datasets, demonstrating DREAM outperforms various SOTA cross-domain recommendation algorithms achieving up to a 75% uplift in Movie-Book Scenarios.

    Full text in ACM Digital Library

  • RESA Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation
    by Zitao Xu (Shenzhen University), Weike Pan (Shenzhen University) and Zhong Ming (Shenzhen University).

    Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences. Despite their success, these works usually suffer from the sparsity problem commonly existed in real applications. Cross-domain sequential recommendation aims to alleviate this problem by introducing relatively richer source-domain data. However, most existing methods capture the users’ preferences independently of each domain, which may neglect the item transition patterns across sequences from different domains, i.e., a user’s interaction in one domain may influence his/her next interaction in other domains. Moreover, the data sparsity problem still exists since some items in the target and source domains are interacted with only a limited number of times. To address these issues, in this paper we propose a generic framework named multi-view graph contrastive learning (MGCL). Specifically, we adopt the contrastive mechanism in an intra-domain item representation view and an inter-domain user preference view. The former is to jointly learn the dynamic sequential information in the user sequence graph and the static collaborative information in the cross-domain global graph, while the latter is to capture the complementary information of the user’s preferences from different domains. Extensive empirical studies on three real-world datasets demonstrate that our MGCL significantly outperforms the state-of-the-art methods.

    Full text in ACM Digital Library

  • RESExploring False Hard Negative Sample in Cross-Domain Recommendation
    by Haokai Ma (Shandong University), Ruobing Xie (WeChat, Tencent), Lei Meng (School of software, Shandong University), Xin Chen (tencent), Xu Zhang (WeChat Search Application Department, Tencent Inc.), Leyu Lin (WeChat Search Application Department, Tencent) and Jie Zhou (Wechat, Tencent).

    Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides the default random samples. However, these hard negative sampling methods usually struggle with false hard negative samples (FHNS), which happens when a user-item interaction has not been observed yet and is picked as a negative sample, while the user will actually interact with this item once exposed to it. Such FHNS issues may seriously confuse the model training, while most conventional hard negative sampling methods do not systematically explore and distinguish FHNS from HNS. To address this issue, we propose a novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross-domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors. For the general part, we conduct the coarse-grained and fine-grained real HNS selectors sequentially, armed with a dynamic item-based FHNS filter to find high-quality HNS. For the cross-domain part, we further design a new cross-domain HNS for alleviating negative transfer in CDR and discover its corresponding FHNS via a dynamic user-based FHNS filter to keep its power. We conduct experiments on four datasets based on three representative model-agnostic hard negative sampling methods, along with extensive model analyses, ablation studies, and universality analyses. The consistent improvements indicate the effectiveness, robustness, and universality of RealHNS, which is also easy-to-deploy in real-world systems as a plug-and-play strategy. The source code will be released in the future.

    Full text in ACM Digital Library

  • RESDomain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
    by Jiajie Zhu (Macquarie University), Yan Wang (Macquarie University), Feng Zhu (Ant Group) and Zhu Sun (Macquarie University).

    The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the recommendation performance on a sparser target domain by transferring the knowledge from a source domain that contains relatively richer information. By contrast, in recent years, dual-target CDR has been proposed to improve the recommendation performance on both domains simultaneously. However, to this end, there are two challenges in dual-target CDR: (1) how to generate both relevant and diverse augmented user representations, and (2) how to effectively decouple domain-independent information from domain-specific information, in addition to domain-shared information, to capture comprehensive user preferences. To address the above two challenges, we propose a Disentanglement-based framework with Interpolative Data Augmentation for dual-target Cross-Domain Recommendation, called DIDA-CDR. In DIDA-CDR, we first propose an interpolative data augmentation approach to generating both relevant and diverse augmented user representations to augment sparser domain and explore potential user preferences. We then propose a disentanglement module to effectively decouple domain-specific and domain-independent information to capture comprehensive user preferences. Both steps significantly contribute to capturing more comprehensive user preferences, thereby improving the recommendation performance on each domain. Extensive experiments conducted on five real-world datasets show the significant superiority of DIDA-CDR over the state-of-the-art methods.

    Full text in ACM Digital Library

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