E-Commerce Recommender

Intelligent Product Recommendations

Hybrid recommendation engine with collaborative filtering and personalized explanations.

Data Input

Input Your Data

Provide your product catalog and user behavior data using natural language, JSON format, or file upload.

Product Catalog

User Behavior

Methodology

Hybrid Recommendation Approach

Adaptive algorithm combining 3-5 methods based on available data, powered by AI explanations for transparent, personalized suggestions.

25% Weight (Multi-User)

User-Based Collaborative

Finds similar users and recommends what they liked using engagement scoring.

  • -User similarity matching
  • -Category interest overlap

20-40% Weight

Item-Based Collaborative

Analyzes user preferences to predict product affinity based on category and price patterns.

  • -Category preference profiling
  • -Price range similarity

20-30% Weight

Content-Based

Matches product attributes weighted by user engagement to find similar items.

  • -Category & tag matching
  • -Price & description similarity

20-30% Weight

Context-Aware

Applies behavioral signals, device context, and time-based rules for smarter recommendations.

  • -Engagement-based scoring
  • -Device & time context

15% Weight (Multi-User)

Category Popularity

Recommends trending products in categories the user is interested in.

  • -Popularity across users
  • -Category interest weighting

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AI-Powered Explanations

Personalized explanations for each recommendation with context and transparency.

-Context-aware reasoning
-Natural language generation

System Overview

3-5

Adaptive Algorithms

100%

Dynamic Recommendations

AI

Powered Insights

Process Flow

1

Input

Data entry

2

Parse

LLM processing

3

Analyze

Hybrid algorithm

4

Explain

AI insights

5

Recommend

Final results