reveiw

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DEPARTMENT OF COMPUTER SCIENCE
ENGINEERING SCHOOL OF COMPUTING 1156CS601- MINOR
PROJECT SUMMER SEMESTER(21-22) REVIEW - I
FASHION RECOMMENDATION SYSTEM USING MACHINE
LEARNING
SUPERVISED BY Faculty NameSANGEETHA.A Designatio
nASSISTANT PROFESSOR
PRESENTED BY 1. P.YOGESH
(VTU.14676)(19UECS0707) 2. M.LOKESH THAMBI
(VTU.14880)(19UECS0592) 3. CH.SIVAPRASAD
(VTU.15735)(19UECS0190)
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AGENDA

• ABSTRACT
• OBJECTIVE
• INTRODUCTION
• LITERATURE REVIEW
• DESIGN AND METHODOLOGIES
• IMPLEMENTATION
• CONCLUSION
• REFERENCES
• WEB REFERENCES
• PLAGIARISM REPORT (should be less than 15)

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ABSTRACT

• In recent years, the textile and fashion
  industries have witnessed an enormous amount of
  growth in fast fashion. On e-commerce platforms,
  where numerous choices are available, an
  efficient recommendation system is required to
  sort, order, and efficiently convey relevant
  product content or information to users.
• Image-based fashion recommendation systems (FRSs)
  have attracted a huge amount of attention from
  fast fashion retailers as they provide a
  personalized shopping experience to consumers.

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OBJECTIVES

• Aim of the Project
• In this work, we make use of machine learning and
  computer vision technologies to automatically
  design new must-have fashion products with
  popular styles discovered from fashion product
  images and historical transaction data.
• The visual-based fashion attributes are learned
  from fashion product images via a deep
  convolutional neural network
• Scope of the Project
• The scope of the project is to build a fashion
  recommendation system capable of learning a
  persons clothing style and preferences by
  extracting the a variety of attributes from
  his/her clothing images. These attributes are
  then fed to a similarity model to retrieve most
  closest similar images as recommendations.

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INTRODUCTION

• Clothing is a kind of symbol that represents
  peoples internal perceptions through their
  social status, and attitude towards life.
• Therefore, clothing is believed to be a nonverbal
  way of communicating and a major part of peoples
  appearance.
• Advancements have enabled consumers to track
  current fashion trends around the globe.
• Factors associated with clothing choices could
  transmit the image features for a better.

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LITERATURE REVIEW

• Kang, W.-C. Fang, C. Wang, Z. McAuley, J.
  Visually-aware fashion recommendation and design
  with generative image models .In Proceedings the
  2017 IEEE International Conference on Data Mining
  (ICDM), New Orleans, LA, USA, 1821 November
  2017 pp. 207216.
• Karmaker Santu, S.K. Sondhi, P. Zhai, C. On
  application of learning to rank for e-commerce
  search. In Proceedings of the 40th International
  ACM SIGIR Conference on Research and Development
  in Information Retrieval, Shinjuku, Tokyo, Japan,
  711 August 2019 pp. 475484.

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LITERATURE REVIEW

• Chakraborty, S. Hoque, S.M.A. Kabir, S.M.F.
  Predicting fashion trend using runway images
  Application of logistic regression In trend
  forecasting. Int. J. Fash. Des. Technol. Educ.
  2020, 13, 376386.
• An important aspect of color forecasting is the
  process of generating color palettes to represent
  collections at fashion shows.
• Humans have traditionally done this manually, and
  can do it well, but there are often too many
  images and it becomes an unmanageable task.
• In this paper, automatic machine-learning methods
  are developed to generate colour palettes for a
  fashion show based on the runway images

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DESIGN AND METHOLOGIES

• MODULE 1
• Data Collection and training using Machine
  Learning Algorithms
• MODULE 2
• Real Time data gathering and prognostics
  GUI design

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MODULE1

• Data Collection and training using Machine
  Learning Algorithms
• Step1 Collection of data

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• Step 2 Processing of data
• Step 3 Apply Machine Learning Algorithms

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• Step 4 Get the Output

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IMPLEMENTATION

• Architecture Diagram
• Data Flow Diagram
• E-R Diagram

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ARCHITECTURE DIAGRAM
fashion recommendation system
techniques
DATA
GET THE OUTPUT
performance
CAUSES
APPLYING ALGORITHMS
EXCELSHEETS
TEST THE OUTPUT
e-commerce
PRE PROCESS THE DATA
NEW DATA
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DATA FLOW DIAGRAM
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ER- DIAGRAM
fashion recommendation system
e-commerce
TEST THE OUTPUT
algorithmic models
filtering technique
DATA BASE
GET THE OUTPUT
PROCESS THE DATA
COLLECTION OF DATA
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CONCLUSION

• Recommendation systems have the potential to
  explore opportunities for retailers by enabling
  them to provide customized recommendations to
  consumers based on information retrieved from the
  Internet
• Therefore, research on embedding social media
  images within fashion recommendation systems has
  gained huge popularity in recent times

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REFERENCES

• Chakraborty, S. Hoque, S.M.A. Kabir, S.M.F.
  Predicting fashion trend using runway images
  Application of logistic regression in trend
  forecasting. Int. J. Fash. Des. Technol. Educ.
  2020, 13, 376386.
• Karmaker Santu, S.K. Sondhi, P. Zhai, C. On
  application of learning to rank for e-commerce
  search. In Proceedings of the 40th International
  ACM SIGIR Conference on Research and Development
  in Information Retrieval, Shinjuku, Tokyo, Japan,
  711 August 2018 pp. 475484
• Garude, D. Khopkar, A. Dhake, M. Laghane, S.
  Maktum, T. Skin-tone and occasion oriented outfit
  recommendation system. SSRN Electron. J. 2019

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• Zhang, Y. Caverlee, J. Instagrammers,
  Fashionistas, and Me Recurrent Fashion
  Recommendation with Implicit Visual Influence. In
  Proceedings of the 28th ACM International
  Conference on Information and Knowledge
  Management, Beijing, China, 37 November 2019
  pp. 15831592
• Tsujita, H. Tsukada, K. Kambara, K. Siio, I.
  Complete fashion coordinator A support system
  for capturing and selecting daily clothes with
  social networks. In Proceedings of the
  International Conference on Advanced Visual
  InterfacesAVI 10, Rome, Italy, 2628 May 2019
  p. 127
• Spiller, L. Tuten, T. Integrating Metrics Across
  the Marketing Curriculum The digital and social
  media opportunity. J. Mark. Educ. 2015, 37,
  114126

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Web References

• GOGGLE
• CHROME
• REFERENCE PAPERS
• SURVEY PAPERS
• INTERNT EXPLORER
• FIRE MOZILLA

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Plagiarism report
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• THANK YOU