Skip to content

Latest commit

 

History

History
123 lines (94 loc) · 6.87 KB

README.md

File metadata and controls

123 lines (94 loc) · 6.87 KB
Raw

Table of Contents

  1. Product Description
  2. State of Implementation
  3. Use Cases
  4. Current Issues and Limitations
  5. Roadmap - Future Scope

Product Description

Elevate your fashion shopping experience with the Conversational Fashion Outfit Generator – a cutting-edge addition to the Flipkart ecosystem. Seamlessly integrated into the platform, our AI-powered system redefines the way you discover, create, and personalize fashion outfits.

Powered by advanced Generative AI technology, our outfit generator engages in natural, human-like conversations to truly understand your style preferences. Leveraging your past purchase history, browsing patterns, and real-time social media trends, we deliver tailored and on-trend outfit recommendations that resonate with your unique fashion taste.

Unveil a world of possibilities as you effortlessly explore personalized outfit suggestions for every occasion. From casual outings to formal events, our generator crafts complete, well-coordinated outfits, including clothing, accessories, and footwear. With the option to interact and fine-tune outfits in a conversational manner, you're in control of your style journey.

State of Implementation

  • Natural Conversational Language Queries to Relevant Outfit Suggestion.
  • User can suggest tweaks to the suggested outfit.
  • Complete Outfit Generation, with a simple input. (e.g. "I will go on a trip to the mountains. Show me everything I need.")

Use Cases

Personalized Outfit Recommendations via Natural Conversations:

  • Enhanced User Engagement: Users interact naturally, describing their style and preferences, receiving outfit recommendations that align seamlessly with their individual tastes.
  • Conversion Rate Impact: Conversational interactions lead to higher conversion rates as users find tailored products that resonate, directly contributing to revenue growth.

Precise Outfit Coordination for Enhanced Styling:

  • Effortless Fashion Coordination: Users receive comprehensive outfit suggestions based on their preferences, taking into account clothing, accessories, and footwear.
  • Upselling and ARPU Boost: Offering complete outfits boosts Average Revenue Per User (ARPU) and Average Order Value(AOV) as users are more likely to purchase multiple items within a well-coordinated ensemble.

Real-time Social Media Trend Integration:

  • On-Trend Recommendations: By tapping into social media trends, users stay ahead of the curve with outfit suggestions aligned to the latest fashion styles.
  • Conversion Rate Enhancement: Trend-sensitive recommendations drive higher conversion rates, capitalizing on users' desire for up-to-the-minute fashion choices.

Customizable Outfit Adjustments for Individual Expression:

  • Tailored Personalization: Users actively engage in conversations to fine-tune outfit recommendations, ensuring the perfect look that aligns with their unique style.
  • User Satisfaction and Retention: Enhanced personalization leads to higher user satisfaction, positively impacting customer retention and lifetime value.

Instant Feedback Loop for Continuous Improvement:

  • User Feedback Integration: Users provide real-time feedback on recommended outfits, fostering an iterative improvement cycle.
  • Conversion Rate and Loyalty: Actively involving users in the enhancement process enhances their sense of ownership, leading to increased loyalty and conversion rates.

Seamless Integration with Flipkart's Ecosystem:

  • Unified Shopping Experience: The Conversational Fashion Outfit Generator seamlessly integrates into Flipkart's existing platform, providing users with a holistic shopping journey.
  • Enhanced Business Metrics: Improved user engagement, conversion rates, and ARPU directly contribute to elevated profitability and key user analytics.

Current Issues and Limitations

  • Uncertain LLM Hallucination
Issue Description Solution
Context Overload and Noise Providing a large amount of data about the user can potentially confuse the Language Model (LLM) and lead to noisy or irrelevant responses. Experimenting with attention mechanisms or summarization techniques to guide the LLM.
Lack of Information Self Explanatory. Not Providing The LLM with enough information will lead to incorrect/misleading suggestions Implement Filter Layers that will evaluate User Requests for Context.
Inadequate Domain Knowledge LLMs may lack domain-specific knowledge about fashion, designers, and specific fashion trends, impacting the accuracy of generated recommendations. Fine-tune LLM with specific domain knowledge and techniques.
  • User Interface

Roadmap - Future Scope

  • Ideating & Decomposition of Problem Statement

    • Figuring out how the solution can be integrate seamlessly into existing Flipkart Infrastructure
    • Efficient Uses of Resources - Approach to leverage existing Product Recommendation Pipeline
    • Defining specific usecases
    • Iterate

  • Data Preparation

    • Collect Sample Flipkart Outfit Inventory Dataset
    • Preprocess and Organize Data.

  • Prototype User Interface

    • Design a basic User Interface
    • Enable Conversational Product Discovery
    • Generate Initial Response
    • Update Suggested Changes

  • Prompt Designing & Engineering

  • Intents Extraction & Search LLM Pipeline

    • Reformat User Request
    • Extract Outfit Suggestions
    • Reformat Suggestions & Search Inventory
    • Iterate and Improve

  • Vector Database Design

  • Virtual Try On

    • Researched Existing Solutions and Proposals
    • Implementation

  • Integrate into Existing Flipkart Ecosystem

    • :)

  • Helpful Features

    • Suggested Frequent Requests
    • Enabled Voice Recognition
    • [ ]