MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation

Xu, Senrong; Li, Liangyue; Yao, Yuan; Chen, Zulong; Wu, Han; Lu, Quan; Tong, Hanghang

doi:10.1145/3539597.3570414

Cited by 7 publications

(2 citation statements)

References 31 publications

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“…Multi-Domain Recommendation (Tan et al 2021;Xu et al 2023;Wang et al 2022;Zhang et al 2022b;Luo et al 2022;Gao et al 2023) aims to capture the commonalities and diversities of various scenarios with a unified model. In recent times, a multitude of relevant endeavors has emerged, propelling the advancement of this field.…”

Section: Related Work Multi-domain Recommendationmentioning

confidence: 99%

D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations

Jia,

Wang,

Lin

et al. 2024

AAAI

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To enhance the efficacy of multi-scenario services in industrial recommendation systems, the emergence of multi-domain recommendation has become prominent, which entails simultaneous modeling of all domains through a unified model, effectively capturing commonalities and differences among them. However, current methods rely on manual domain partitioning, which overlook the intricate domain relationships and the heterogeneity of different domains during joint optimization, hindering the integration of domain commonalities and differences. To address these challenges, this paper proposes a universal and flexible framework D3 aimed at optimizing the multi-domain recommendation pipeline from three key aspects. Firstly, an attention-based domain adaptation module is introduced to automatically identify and incorporate domain-sensitive features during training. Secondly, we propose a fusion gate module that enables the seamless integration of commonalities and diversities among domains, allowing for implicit characterization of intricate domain relationships. Lastly, we tackle the issue of joint optimization by deriving loss weights from two complementary viewpoints: domain complexity and domain specificity, alleviating inconsistencies among different domains during the training phase. Experiments on three public datasets demonstrate the effectiveness and superiority of our proposed framework. In addition, D3 has been implemented on a real-life, high-traffic internet platform catering to millions of users daily.

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Section: Related Work Multi-domain Recommendationmentioning

confidence: 99%

D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations

Jia,

Wang,

Lin

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

“…Some works correct the update direction for each client to reduce the data drift in non-IID setting (Karimireddy et al 2020;Zhang et al 2021b). Other works align the prototype of heterogeneous clients to enforce the learning for global extractor with less communication cost (Tan et al 2022b;Xu, Tong, and Huang 2023).…”

Section: Related Workmentioning

confidence: 99%

Knowledge-Aware Parameter Coaching for Personalized Federated Learning

Zhi,

Bi,

et al. 2024

AAAI

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Personalized Federated Learning (pFL) can effectively exploit the non-IID data from distributed clients by customizing personalized models. Existing pFL methods either simply take the local model as a whole for aggregation or require significant training overhead to induce the inter-client personalized weights, and thus clients cannot efficiently exploit the mutually relevant knowledge from each other. In this paper, we propose a knowledge-aware parameter coaching scheme where each client can swiftly and granularly refer to parameters of other clients to guide the local training, whereby accurate personalized client models can be efficiently produced without contradictory knowledge. Specifically, a novel regularizer is designed to conduct layer-wise parameters coaching via a relation cube, which is constructed based on the knowledge represented by the layered parameters among all clients. Then, we develop an optimization method to update the relation cube and the parameters of each client. It is theoretically demonstrated that the convergence of the proposed method can be guaranteed under both convex and non-convex settings. Extensive experiments are conducted over various datasets, which show that the proposed method can achieve better performance compared with the state-of-the-art baselines in terms of accuracy and convergence speed.

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MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

Wang,

Sharifi,

Gadekallu

et al. 2024

Int J Comput Intell Syst

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Music plays a vital role in human culture and society, serving as a universal form of expression. However, accurately classifying music emotions remains challenging due to the intricate nature of emotional expressions in music and the integration of diverse data sources. To address these challenges, we propose the Multilayered Music Decomposition and Multimodal Integration Interaction (MMD-MII) model. This model employs cross-processing to facilitate interaction between audio and lyrics, ensuring coherence in emotional representation. Additionally, we introduce a hierarchical framework based on the music theory, focusing on the main and chorus sections, with the chorus processed separately to extract precise emotional representations. Experimental results on the DEAM and FMA datasets demonstrate the effectiveness of the MMD-MII model, achieving accuracies of 49.68% and 49.54% respectively. Compared with the existing methods, our model outperforms in accuracy and F1 scores, offering promising implications for music recommendation systems, healthcare, psychology, and advertising, where accurate emotional analysis is essential.

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MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation

Cited by 7 publications

References 31 publications

D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations

D3: A Methodological Exploration of Domain Division, Modeling, and Balance in Multi-Domain Recommendations

Knowledge-Aware Parameter Coaching for Personalized Federated Learning

MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

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