A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T–S fuzzy neural network

Wong, Wai Keung; Zeng, Xianyi; Au, Wai Man

doi:10.1016/j.eswa.2007.12.068

Cited by 37 publications

(20 citation statements)

References 31 publications

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“…GA, on the other hand, is commonly used to solve optimization problems, including production or manufacturing scheduling of apparel retail supply chain. However, it has been criticized for the shortcomings of huge computation time and slow convergence near the optimum [12].…”

Section: Introductionmentioning

confidence: 99%

Item-level RFID for enhancement of customer shopping experience in apparel retail

Choi

Yang

et al. 2015

Computers in Industry

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In the customer-oriented apparel retail industry, providing satisfactory shopping experience for customers is a vital differentiator. However, traditional stores generally cannot fully satisfy customer needs because of difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. Therefore, this paper proposes an item-level RFID-enabled retail store management system for relatively high-end apparel products to provide customers with more leisure, interaction for product information, and automatic apparel collocation to promote sales during shopping. In this system, RFID hardware devices are installed to capture customer shopping behaviour and preferences, which would be especially useful for business decision-making and proactive individual marketing to enhance retail business. Intelligent fuzzy screening algorithms are then developed to promote apparel collocation based on the customer preferences, the design features of products, and the sales history accumulated in the database. It is expected that the proposed system, when fully implemented, can help promote retail business by enriching customers with intelligent and personalized services, and thus enhance the overall shopping experience.

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Section: Introductionmentioning

confidence: 99%

Item-level RFID for enhancement of customer shopping experience in apparel retail

Choi

Yang

et al. 2015

Computers in Industry

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“…Occasions have been mentioned in many studies (Yu-Chu et al, 2012, Wong et al, 2009b, Wong et al, 2009a, Vogiatzis et al, 2012, Cheng and Liu, 2008. A scenario-oriented recommendation system was introduced to match apparel with daily scenarios by a semantic network based on common sense reasoning technology-Open Mind Common Sense (OMCS), which contains over 800,000 English sentences about common sense (Shen et al, 2007).…”

Section: The Research Gap In User Profilesmentioning

confidence: 99%

“…For instance, an apparel expert system integrated knowledge of fashion designers, with T-S fuzzy neural network method to learn the expertise of attribute evaluation. It created an expert knowledge database from symbolic inputs to linguistic outputs through an inference engine (Wong et al, 2009a, Wong et al, 2009b. Based on expert rules, a study proposed an intelligent apparel recommendation expert system by positive rule reasoning mechanism (Dong et al, 2013).…”

Section: Intelligent Recommendation Systemsmentioning

confidence: 99%

Apparel recommendation system evolution: an empirical review

Guan

Qin

Ling

et al. 2016

IJCST

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PurposeWith the developments of e-commerce markets, novel recommendation technologies are becoming an essential part of many online retailers' economic models to help drive online sales. Initially, this paper undertakes an investigation of apparel recommendations in the commercial market in order to verify the research value and significance. Then, this article reviews apparel recommendation techniques and systems through academic research, aiming to acquaint apparel recommendation context, summarize the pros and cons of various research methods, identify research gaps, and eventually propose new research solutions to benefit apparel retailing market. Design/methodology/approachThis study utilizes empirical research drawing on 130 academic publications indexed from online Databases. We introduce a three-layer descriptor for searching articles, and analyse retrieval results via distribution graphics of years, publications, and keywords. FindingsThis study classified high-tech integrated apparel systems into 3D CAD systems, personalized design systems, and recommendation systems. Our research interest is focused on recommendation system. Four types of models were found, namely clothes searching/retrieval, wardrobe recommendation, fashion coordination and intelligent recommendation systems. The forth type, smart systems, has raised more awareness in apparel research as it is equipped with advanced functions and application scenarios to satisfy customers. Despite various computational algorithms were tested in system modelling, existing research lacks of concerns in terms of apparel and users profiles research. Thus, from the review, we have identified and proposed a more complete set of key features for describing both apparel and users profiles in a recommendation system. Originality/valueBased on previous studies, this is the first review paper on this topic in this subject field. The summarised work and the proposed new research will inspire future researchers with various knowledge backgrounds, especially, from a design perspective.

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“…GA has been widely used for eliciting fuzzy models owing to its ability to search for optimal solutions in high-dimensional solution spaces. GA is a global optimizer based on the concepts of natural evolution [7], however when being used in a generic form it may lead to a significant computing overhead and slow convergence caused by the need to explore a huge search space [11]. To eliminate this overhead and increase the effectiveness of the underlying optimization, we introduce dynamic search-based GA that results in a rapid convergence while narrowing down the search to a limited region of the search space.…”

Section: Introductionmentioning

confidence: 99%

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

Park¹,

Kim²,

Oh³

2013

Journal of Electrical Engineering and Technology

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-In this paper, we introduce advanced architectures of genetically-oriented Fuzzy Neural Networks (FNNs) based on fuzzy set and fuzzy relation and discuss a comprehensive design methodology. The proposed FNNs are based on 'if-then' rule-based networks with the extended structure of the premise and the consequence parts of the fuzzy rules. We consider two types of the FNNs topologies, called here FSNN and FRNN, depending upon the usage of inputs in the premise of fuzzy rules. Three different type of polynomials function (namely, constant, linear, and quadratic) are used to construct the consequence of the rules. In order to improve the accuracy of FNNs, the structure and the parameters are optimized by making use of genetic algorithms (GAs). We enhance the search capabilities of the GAs by introducing the dynamic variants of genetic optimization. It fully exploits the processing capabilities of the FNNs by supporting their structural and parametric optimization. To evaluate the performance of the proposed FNNs, we exploit a suite of several representative numerical examples and its experimental results are compared with those reported in the previous studies.

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A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T–S fuzzy neural network

Cited by 37 publications

References 31 publications

Item-level RFID for enhancement of customer shopping experience in apparel retail

Item-level RFID for enhancement of customer shopping experience in apparel retail

Apparel recommendation system evolution: an empirical review

A Design of Dynamically Simultaneous Search GA-based Fuzzy Neural Networks: Comparative Analysis and Interpretation

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