Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System

Yadav, Naina; Singh, Anil Kumar

doi:10.1145/3441501.3441503

Cited by 9 publications

(2 citation statements)

References 14 publications

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“…Earlier, many retrieval and mining algorithms were proposed for recommendation generation, which only considers the most relevant and top-rated items for a recommendation from a large set of items. For instance, suggesting highly rated movies to users, the most listened to songs for the user and the most visited news article for the recommendation [24]. Various ranking methodologies are proposed for diversity in these systems, which re-rank items for diversification in the recommendation list [19,5,1].…”

Section: Related Workmentioning

confidence: 99%

Item Enhanced Diversification in the Recommendation System using Graph Neural Network

Yadav,

kumar,

Singh

2024

Machine Learning, IOT and Blockchain

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A recommendation system is a set of programs that utilize different methodologies for relevant item selection for the user. Graph neural networks have been extensively used in recent years to improve the quality of recommendations across all domains. A general recommendation system’s main goal is to recommend items to the user accurately, and it frequently prioritizes items that are well-liked or mainstream. If the model concentrates only on one specific item category from the users’ past preferences, then recommendations for the target user will become too obvious. To address this problem, diversity in the recommendation system is introduced. The model IG-DivRS (Item-Enhanced Graph Neural Network for a Diversified Recommendation System) is proposed. Our proposed model uses a Graph Neural Network (GNN) with the user’s interacted and non-interacted item history for diversified recommendation generation. The novelty of our proposed model is to explore the effect of non-interacted items on the target user for diversified recommendation generation. Instead of selecting random non-interacted items for the target user, we apply the DPP(Determinantal Point Process) algorithm to select the non-interacted item appropriately. The detailed experimental analysis shows that our model ID-DivRS outperforms the state-of-the-art model in accuracy and diversity.

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Section: Related Workmentioning

confidence: 99%

Item Enhanced Diversification in the Recommendation System using Graph Neural Network

Yadav,

kumar,

Singh

2024

Machine Learning, IOT and Blockchain

View full text Add to dashboard Cite

show abstract

“…Therefore, a generalized commercial AI engine requires an NLP-based model that allows for the flexible interpretation of data. Sun et al (2019), Yadav and Singh (2020), Lian and Li (2020), and Moreira et al (2021) utilized a Transformer (Vaswani et al 2017) to create recommendation systems; however, they were not NLP-based. Prior research has employed NLP methodology to construct product-level recommendation systems; however, there has been limited application of utilizing both merchants and product names within the context of natural language processing.…”

Section: Introductionmentioning

confidence: 99%

General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants

Lee,

Jeong,

Kim

et al. 2024

AAAI

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One of the most crucial capabilities in the commercial sector is a personalized prediction of a customer's next purchase. We present a novel method of creating a commerce intelligence engine that caters to multiple merchants intended for the UB Platform, managed by e-payment company Harex InfoTech. To cultivate this intelligence, we utilized payment receipt data and created a Natural Language Processing (NLP)-based commerce model using a Transformer to accommodate multinational and merchant trade. Our model, called General Commerce Intelligence (GCI), provides a range of services for merchants, including product recommendations, product brainstorming, product bundling, event promotions, collaborative marketing, target marketing, and demand fore-casting etc. To bolster user privacy and foster sustainable business collaboration, especially among micro-, small-, and medium-sized enterprises (MSMEs), the GCI model was trained through federated learning, especially with glocalization. This study delves into the structure, development, and assessment of GCI, showcasing its transformative capacity to implement User Centric AI and re-shape the global commerce landscape to benefit MSMEs.

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Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT+NBSVM

Umair

Masciari

Madeo³

et al. 2023

Communications in Computer and Information Science

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Bi-directional Encoder Representation of Transformer model for Sequential Music Recommender System

Cited by 9 publications

References 14 publications

Item Enhanced Diversification in the Recommendation System using Graph Neural Network

Item Enhanced Diversification in the Recommendation System using Graph Neural Network

General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants

Sentimental Analysis of COVID-19 Vaccine Tweets Using BERT+NBSVM

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