Search behavior alone is not enough.
A search for “shoes” does not reveal whether the user prefers minimal sneakers, formal leather shoes, trending designs, neutral colors, or budget-friendly options.
← Back to landing
Maz Style
Style Finder
A taste and style intelligence layer that helps Mazzaneh recommend more relevant, personal, and high-intent fashion products.
Style Finder analyzes user behavior, searches, saved items, purchases, visual inputs, and content interactions to understand each user’s personal style. It helps Mazzaneh identify what users like, what patterns they repeat, and which products are most likely to match their taste.
The Problem
Without taste intelligence, product discovery becomes a generic catalog.
Fashion shoppers often face too many products. Many items may be good in quality or price, but they do not match the user’s real taste. Most systems can see what users search or buy, but they do not always understand why a product attracted them.
Generic recommendations create friction.
When recommendations are not aligned with the user’s style, the user spends more time filtering, browsing, and ignoring irrelevant products.
Does the user care more about color, shape, brand, price, material, or occasion?
Is the user closer to a minimal, classic, formal, casual, sporty, streetwear, luxury, or trend-driven style?
Which patterns in behavior show real purchase intent instead of casual browsing?
The Solution
Style Finder turns user behavior into a usable taste profile.
Style Finder analyzes searches, viewed products, saved items, purchases, content interactions, selections, and visual inputs to build a dynamic profile of each user’s personal style.
User Behavior
Searches, views, saves, purchases, visual inputs, and content interactions.
Taste Profile
The system detects recurring patterns in style, color, shape, budget, and intent.
Personalized Discovery
Products, sellers, styles, and search results become more relevant to each user.
How It Works
From scattered signals to a living style profile.
Style Finder continuously learns from user actions and improves the relevance of discovery, search ranking, and seller-product matching.
01
Collect Behavioral Signals
The system observes searches, clicks, viewed products, saved items, purchases, content interactions, and other in-platform behavior.
02
Analyze Preferred Attributes
Style Finder identifies repeated patterns in color, category, shape, price range, brand interest, material, style type, and occasion.
03
Build a Taste Profile
The system creates a dynamic profile that represents the user’s personal style and updates it with every new interaction.
04
Personalize Recommendations
Products, sellers, search results, styles, and content can be ranked and recommended based on the user’s taste profile.
05
Keep Learning
Every save, view, rejection, purchase, and visual input helps Mazzaneh understand the user’s style more accurately.
Key Capabilities
A complete style understanding layer for personalized fashion discovery.
Style Finder is not just search. It combines behavior analysis, preference detection, visual inputs, and product ranking to make discovery feel more personal.
Search Behavior Analysis
Understand what users search, which categories they repeat, and what patterns appear across their discovery journey.
Viewed Product Analysis
Use product views and engagement depth as signals for what visually or functionally attracts the user.
Saved Item Analysis
Saved and liked products act as stronger taste signals that help identify style direction and purchase interest.
Purchase History Analysis
Past purchases show what users not only like, but are willing to pay for.
Dominant Style Detection
Detect style tendencies such as minimal, classic, formal, casual, sporty, streetwear, luxury, or trend-driven.
Color, Shape, and Material Analysis
Identify recurring preferences in color palettes, product forms, materials, and design details.
Relevant Product Recommendations
Recommend products from Mazzaneh sellers that better match the user’s personal taste profile.
Visual Search
Allow users to use a photo or screenshot to find similar products and add visual inputs to their taste profile.
Personalized Search Ranking
Rank search results differently for different users based on what each person is likely to prefer.
Adjacent Style Discovery
Suggest nearby styles that are close to the user’s current taste but still create room for discovery.
Value
Style Finder improves relevance across the entire shopping experience.
By understanding personal style, Mazzaneh can reduce irrelevant browsing, increase recommendation quality, and connect seller products to better-matched users.
For Users
Users spend less time filtering irrelevant products and discover items that feel closer to their own taste, lifestyle, and shopping intent.
For Mazzaneh
Mazzaneh gains a behavioral and taste intelligence layer that improves personalization, engagement, and conversion potential.
For Sellers
Seller products can be shown to users whose style, budget, and shopping patterns make them more relevant prospects.
Example Scenario
Style Finder turns scattered behavior into a usable style signal.
A simple example shows how user actions can be transformed into a clearer understanding of personal taste.
User behavior
Over several days, the user repeatedly searches for white sneakers, views simple black jackets, saves minimal products, and spends more time on solid-color items.
White sneakers
Black jackets
Minimal products
Neutral colors
Solid designs
Style Finder output
Mazzaneh identifies a likely preference for minimal style, neutral colors, simple forms, and everyday wearable products. It can then recommend clean white sneakers, minimal jackets, simple T-shirts, straight-leg pants, and sellers that match this style direction.
Data Inputs
Style Finder learns from multiple signals across Mazzaneh.
The more relevant signals the system receives, the more accurate and useful the taste profile becomes.
Search queries
Clicks
Viewed products
View duration
Saved items
Past purchases
Favorite categories
Preferred price ranges
Recurring colors
Brand interest
Visual inputs
Content interactions
My Closet data
My Size data
Integration
Style Finder is the taste understanding brain inside Maz Style.
It connects with My Closet, My Size, and seller inventory to make recommendations more personal, practical, and commercially relevant.
Style Finder + My Closet
Style Finder shows what the user likes. My Closet shows what the user owns. Together, they support recommendations that are both desirable and usable.
Style Finder
Taste understanding layer
Style Finder + My Size
My Size ensures that products matching the user’s taste also match their fit and size preferences.
Seller Inventory
Seller inventory can be ranked and recommended based on the user’s taste profile, improving relevance and purchase intent.
Why It Matters
Style Finder helps Mazzaneh understand each user as a person with a distinct style, not just as a shopper with a history.
It makes product discovery more personal, recommendations more accurate, and seller matching more relevant. By turning user behavior into taste intelligence, Style Finder becomes the foundation for smarter fashion discovery and higher-intent shopping.
Style Finder turns search and behavior into taste intelligence — the foundation for smarter recommendations and more intentional purchases in Mazzaneh.