Multi Clustering Recommendation System for Fashion Retail

Bellini, Pierfrancesco; Palesi, Luciano Alessandro Ipsaro; Nesi, Paolo; Pantaleo, Gianni

doi:10.1007/s11042-021-11837-5

Cited by 26 publications

(10 citation statements)

References 60 publications

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“…In the rural tourism marketing system, system design is based on demand analysis, planning the overall architecture to ensure that the system can effectively address marketing challenges and meet user needs. Regarding module division, it mainly covers user interface, data management, recommendation system, and marketing analysis [33][34][35][36][37]. The rationality and accuracy of module division directly affect the system's usability and flexibility.…”

Section: A the Demand And System Design Analysis Of Rural Tourism Mar...mentioning

confidence: 99%

Design and Development of a Rural Tourism Marketing System Using Deep Learning

Yang,

Ren

2024

IEEE Access

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This work aims to explore the optimization effects of marketing in the field of rural tourism with the support of artificial intelligence technologies such as deep learning. It first conducts a framework analysis of the requirements of the rural tourism marketing system, focusing on the recommendation system module. Subsequently, the RippleNet network is introduced, incorporating a scenic knowledge graph into the recommendation model. Simultaneously, the Spatiotemporal Graph Convolutional Attention Network (STGCAN) algorithm is introduced to build a rural tourism recommendation system based on RippleNet integrating STGCAN. Experimental results demonstrate that the system has achieved significant success in terms of loss value, prediction accuracy, recall, and F1 value, with an accuracy of 92.64%, recall of 89.65%, and F1 value of 92.12%, surpassing baseline algorithms such as Convolutional Neural Network (CNN). Additionally, the runtime of the proposed algorithm is significantly lower than that of other models, with a runtime of only 18.84 seconds for a data volume of 3000 records. Therefore, the proposed rural tourism recommendation system exhibits superior predictive performance, providing robust support for the optimization of marketing strategies in the tourism industry.

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Section: A the Demand And System Design Analysis Of Rural Tourism Mar...mentioning

confidence: 99%

Design and Development of a Rural Tourism Marketing System Using Deep Learning

Yang,

Ren

2024

IEEE Access

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“…Distance measurements, centroids, etc., are frequently used. Clustering has applications across many disciplines, including data mining, statistics, biology, and machine learning [7][8][9][10].…”

Section: Related Workmentioning

confidence: 99%

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Maddala,

Sreedevi

2023

RAiSE-2023

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“…Start RS [14] x [15] x [16] x [17] x [18] x [19] x [20] x x x x [21] x [22] x x x [23] x [24] x [9] x [25] x [26] x [27] x [28] x [29] x [30] x [31] x…”

Section: Cold-mentioning

confidence: 99%

“…For example, Ref. [28] proposed the use of a multi clustering recommendation system to predict new customer behaviour. The approach starts by clustering items, followed by a customer feature engineering, allowing the segmentation of users and finalising with item recommendations.…”

Section: Cold-mentioning

confidence: 99%

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

Fernandes,

Miguéis,

Pereira

et al. 2023

Applied Sciences

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Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.

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Multi Clustering Recommendation System for Fashion Retail

Cited by 26 publications

References 60 publications

Design and Development of a Rural Tourism Marketing System Using Deep Learning

Design and Development of a Rural Tourism Marketing System Using Deep Learning

CNAIS: Performance Analysis of the Clustering of Non-Associated Items Set Techniques

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

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