Perceive Your Users in Depth

Ni, Yabo; Ou, Dan; Liu, Shichen; Li, Xiang; Ou, Wenwu; Zeng, Anxiang; Si, Luo

doi:10.1145/3219819.3219828

Cited by 83 publications

(9 citation statements)

References 32 publications

(28 reference statements)

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“…However, excessive multi-hops make it easy for the training model to overfit and affect the recommendation results. DUPN [19] uses LSTM to model the user behavior sequence, then uses it for user modeling, and finally learns common user representations on multiple tasks. But DUPN is not suitable for most recommendation scenarios (such as news recommendations) because it relies on a complete sequence of user behavior.…”

Section: A Kg-aware Recommendation Methodsmentioning

confidence: 99%

MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

Shi

Yang

2023

IEEE Access

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The emergence of recommender system is aimed at solving the problems brought by information explosion to human life and even the development of human society. As a traditional recommendation technique, collaborative filtering often encounters sparsity and cold start problems in many recommendation scenarios. Therefore, researchers have found that the introduction of side information can solve these problems to a certain extent and improve the performance of recommender systems. The knowledge graph is a heterogeneous graph that contains rich semantic relationships among items. The Multi-Perspective Learning based on Transformer Knowledge Graph Enhanced Recommendation (MPL-TransKR) proposed in this paper uses the knowledge graph as the side information for input and introduces the multihead self-attention mechanism by reasonably combining the transformer idea. While learning the high-order neighborhood information of the items, the long-distance information between the items is captured, and the weight value is assigned to the user through the attention mechanism to strengthen the user representation to realize user-item multi-perspective learning to enhance the performance of the recommendation model. Through extensive experiments using public datasets, we demonstrated that MPL-TransKR performs well in book and music recommendations, surpassing state-of-the-art baselines on several metrics.

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Section: A Kg-aware Recommendation Methodsmentioning

confidence: 99%

MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

Shi

Yang

2023

IEEE Access

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“…Existing studies mainly adopt attention mechanisms. DUPN [45] integrates multi-task learning, attention along with RNNs to extract general features that will be shared among the associated tasks. MRAN [82] proposes to use an attention mechanism for feature interaction and task-feature alignment.…”

Section: Parallelmentioning

confidence: 99%

Multi-Task Deep Recommender Systems: A Survey

Wang¹,

Lam²,

Wong³

et al. 2023

Preprint

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Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic in recommendation due to the demand for multi-task prediction considering performance and efficiency. Although MTL has been well studied and developed, there is still a lack of systematic review in the recommendation community. To fill the gap, we provide a comprehensive review of existing multi-task deep recommender systems (MTDRS) in this survey. To be specific, the problem definition of MTDRS is first given, and it is compared with other related areas. Next, the development of MTDRS is depicted and the taxonomy is introduced from the task relation and methodology aspects. Specifically, the task relation is categorized into parallel, cascaded, and auxiliary with main, while the methodology is grouped into parameter sharing, optimization, and training mechanism. The survey concludes by summarizing the application and public datasets of MTDRS and highlighting the challenges and future directions of the field.

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“…DeepFM [13] uses Factorization Machines (FM) instead of Logistic Regression (LR) in the wide-area part to automatically learn second-order feature interactions, while DCN [14] uses a cross-network to learn higher-order representations. DUPN [15] learns common user representations across multiple search and recommendation tasks for more effective personalization. MA-RDPG [16] improves the overall performance of ranking strategies in search, recommendation, and advertising through multi-agent reinforcement learning.…”

Section: Cvr Modelingmentioning

confidence: 99%

Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

Luo¹,

Zhou²,

Ding³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Online advertising has gained much attention on various platforms as a hugely lucrative market. In promoting content and advertisements in real life, the acquisition of user target actions is usually a multi-step process, such as impres-sion→click→conversion, which means the process from the delivery of the recommended item to the user's click to the final conversion. Due to data sparsity or sample selection bias, it is difficult for the trained model to achieve the business goal of the target campaign. Multi-task learning, a classical solution to this problem, aims to generalize better on the original task given several related tasks by exploiting the knowledge between tasks to share the same feature and label space. Adaptively learned task relations bring better performance to make full use of the correlation between tasks. We train a general model capable of capturing the relationships between various tasks on all existing active tasks from a meta-learning perspective. In addition, this paper proposes a Multi-task Attention Network (MAN) to identify commonalities and differences between tasks in the feature space. The model performance is improved by explicitly learning the stacking of task relationships in the label space. To illustrate the effectiveness of our method, experiments are conducted on Alibaba Click and Conversion Prediction (Ali-CCP) dataset. Experimental results show that the method outperforms the state-of-the-art multi-task learning methods.

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Perceive Your Users in Depth

Cited by 83 publications

References 32 publications

MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

MPL-TransKR: Multi-Perspective Learning Based on Transformer Knowledge Graph Enhanced Recommendation

Multi-Task Deep Recommender Systems: A Survey

Multi-Task Deep Learning with Task Attention for Post-Click Conversion Rate Prediction

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