Fix product matching for Modern Minimalist Shelf Decor variants
Enhanced product matching algorithm to properly handle: - Large vs Small size variants for shelf decor products - Improved size extraction from product titles - Higher confidence thresholds for shelf decor products to prevent wrong matches - Better handling of 3D-Printed product variations - Enhanced logging for debugging matching issues This fixes the issue where packing slips would create duplicate products instead of matching to existing Large/Small variants of the same product.
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dlawler489
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ec1d204c36
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1 changed files with 193 additions and 23 deletions
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@ -29,6 +29,7 @@ export interface MatchingResult {
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/**
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* Match order items against existing products in the database
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* Considers size as a factor but ignores color variations
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* Enhanced for Modern Minimalist Shelf Decor products with Large/Small variants
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*/
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export const matchOrderItemsToProducts = (
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orderItems: any[],
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@ -41,44 +42,103 @@ export const matchOrderItemsToProducts = (
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let bestMatch: any = null;
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let bestConfidence = 0;
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// Extract size from item title if present
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const itemSizeMatch = item.title.match(/Size:\s*([^,\s]+(?:\s+[^,\s]+)*)/i);
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const itemSize = itemSizeMatch ? itemSizeMatch[1].trim().toLowerCase() : '';
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// Enhanced size extraction - look for "Large" or "Small" specifically
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const itemTitle = item.title.toLowerCase();
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let itemSize = '';
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// Clean item title for comparison (remove color but keep size info)
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// Check for Large/Small in various patterns
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if (itemTitle.includes('large') && !itemTitle.includes('small')) {
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itemSize = 'large';
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} else if (itemTitle.includes('small') && !itemTitle.includes('large')) {
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itemSize = 'small';
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}
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// Also check for "Size: Large" pattern
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const sizeMatch = item.title.match(/Size:\s*(Large|Small|Med|Medium)/i);
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if (sizeMatch) {
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itemSize = sizeMatch[1].toLowerCase();
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if (itemSize === 'med' || itemSize === 'medium') itemSize = 'med';
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}
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// Clean item title for comparison (remove color and size info for base matching)
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const cleanItemTitle = item.title
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.replace(/Colour:\s*[^,\s]+(?:\s+[^,\s]+)*/i, '') // Remove color
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.replace(/Colour:\s*[^,\s-]+(?:\s+[^,\s-]+)*/i, '') // Remove color
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.replace(/Size:\s*[^,\s-]+/i, '') // Remove explicit size
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.replace(/3D-Printed/i, '') // Remove 3D-Printed as it's common
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.replace(/\s+/g, ' ')
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.trim()
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.toLowerCase();
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console.log('Matching item:', cleanItemTitle, 'Size:', itemSize);
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console.log('🔍 Matching item:', item.title);
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console.log(' Clean title:', cleanItemTitle);
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console.log(' Extracted size:', itemSize);
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for (const product of products) {
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const productTitle = product.title.toLowerCase();
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let productSize = '';
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// Extract size from product title
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if (productTitle.includes('large') && !productTitle.includes('small')) {
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productSize = 'large';
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} else if (productTitle.includes('small') && !productTitle.includes('large')) {
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productSize = 'small';
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} else if (productTitle.includes('med')) {
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productSize = 'med';
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}
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// Clean product title for comparison
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const cleanProductTitle = product.title
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.replace(/Colour:\s*[^,\s-]+(?:\s+[^,\s-]+)*/i, '')
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.replace(/Size:\s*[^,\s-]+/i, '')
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.replace(/3D-Printed/i, '')
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.replace(/\s+/g, ' ')
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.trim()
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.toLowerCase();
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const productSize = product.size ? product.size.toLowerCase() : '';
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// Calculate title similarity (basic word matching)
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const titleSimilarity = calculateTitleSimilarity(cleanItemTitle, cleanProductTitle);
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// Size matching bonus
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// Size matching - this is crucial for your shelf decor products
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let sizeSimilarity = 0;
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if (itemSize && productSize) {
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sizeSimilarity = itemSize === productSize ? 1 : 0;
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console.log(` Size match: ${itemSize} vs ${productSize} = ${sizeSimilarity}`);
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} else if (!itemSize && !productSize) {
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sizeSimilarity = 0.5; // Neutral if neither has size
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sizeSimilarity = 0.7; // Both have no size specified
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} else if (itemSize && !productSize) {
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// Item has size but product doesn't - penalize slightly
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sizeSimilarity = 0.3;
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} else {
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// Product has size but item doesn't - penalize more
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sizeSimilarity = 0.1;
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}
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// Combined confidence score
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const confidence = (titleSimilarity * 0.8) + (sizeSimilarity * 0.2);
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// For shelf decor products, size is very important
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const isShelfDecor = cleanItemTitle.includes('shelf decor') || cleanProductTitle.includes('shelf decor');
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let confidence;
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console.log(`Product: ${cleanProductTitle}, Title sim: ${titleSimilarity}, Size sim: ${sizeSimilarity}, Confidence: ${confidence}`);
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if (isShelfDecor && itemSize && productSize) {
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// For shelf decor with both sizes specified, size match is critical
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confidence = (titleSimilarity * 0.6) + (sizeSimilarity * 0.4);
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} else if (isShelfDecor) {
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// For shelf decor without clear size info, rely more on title
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confidence = (titleSimilarity * 0.8) + (sizeSimilarity * 0.2);
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} else {
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// For other products, standard weighting
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confidence = (titleSimilarity * 0.8) + (sizeSimilarity * 0.2);
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}
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if (confidence > bestConfidence && confidence >= 0.5) { // Fixed: >= instead of > for exact threshold matches
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console.log(` Product: ${product.title}`);
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console.log(` Clean: ${cleanProductTitle}`);
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console.log(` Product size: ${productSize}`);
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console.log(` Title similarity: ${titleSimilarity.toFixed(3)}`);
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console.log(` Size similarity: ${sizeSimilarity}`);
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console.log(` Final confidence: ${confidence.toFixed(3)}`);
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// Require higher confidence for shelf decor products to avoid wrong matches
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const minConfidence = isShelfDecor ? 0.7 : 0.5;
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if (confidence > bestConfidence && confidence >= minConfidence) {
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bestMatch = product;
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bestConfidence = confidence;
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}
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@ -117,29 +177,52 @@ export const matchOrderItemsToProducts = (
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/**
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* Calculate similarity between two product titles
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* Uses word matching and common substring analysis
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* Enhanced for Modern Minimalist Shelf Decor and similar products
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*/
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const calculateTitleSimilarity = (title1: string, title2: string): number => {
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// Split into words and filter out common stop words
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const stopWords = ['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', '-', '–'];
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const stopWords = ['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', '-', '–', '3d', 'printed'];
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const words1 = title1.split(/\s+/).filter(word =>
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word.length > 2 && !stopWords.includes(word)
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word.length > 2 && !stopWords.includes(word.toLowerCase())
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);
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const words2 = title2.split(/\s+/).filter(word =>
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word.length > 2 && !stopWords.includes(word)
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word.length > 2 && !stopWords.includes(word.toLowerCase())
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);
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if (words1.length === 0 || words2.length === 0) return 0;
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// Count matching words
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// For shelf decor products, check for key identifying words
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const isShelfDecor1 = title1.includes('shelf') || title1.includes('decor');
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const isShelfDecor2 = title2.includes('shelf') || title2.includes('decor');
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if (isShelfDecor1 && isShelfDecor2) {
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// Special handling for shelf decor products
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const keyWords = ['modern', 'minimalist', 'shelf', 'decor', 'water', 'reservoir', 'bookend', 'planter'];
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let keyWordMatches = 0;
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for (const keyWord of keyWords) {
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const inTitle1 = title1.includes(keyWord);
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const inTitle2 = title2.includes(keyWord);
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if (inTitle1 && inTitle2) {
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keyWordMatches++;
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}
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}
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// If most key words match, it's likely the same product family
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if (keyWordMatches >= 4) {
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return 0.9; // High similarity for same product family
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}
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}
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// Count matching words with fuzzy matching
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let matchingWords = 0;
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for (const word1 of words1) {
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for (const word2 of words2) {
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if (word1 === word2 ||
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word1.includes(word2) ||
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word2.includes(word1) ||
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levenshteinDistance(word1, word2) <= Math.max(1, Math.min(word1.length, word2.length) * 0.2)) {
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if (word1.toLowerCase() === word2.toLowerCase() ||
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word1.toLowerCase().includes(word2.toLowerCase()) ||
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word2.toLowerCase().includes(word1.toLowerCase()) ||
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levenshteinDistance(word1.toLowerCase(), word2.toLowerCase()) <= Math.max(1, Math.min(word1.length, word2.length) * 0.2)) {
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matchingWords++;
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break;
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}
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@ -151,6 +234,93 @@ const calculateTitleSimilarity = (title1: string, title2: string): number => {
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return matchingWords / totalUniqueWords;
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};
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/**
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* Find potential matches for a missing product
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* This helps suggest existing products that might be similar
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*/
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export const findPotentialMatches = (
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missingProductTitle: string,
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existingProducts: any[],
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maxSuggestions: number = 5
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): Array<{product: any; confidence: number; reason: string}> => {
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const suggestions: Array<{product: any; confidence: number; reason: string}> = [];
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const itemTitle = missingProductTitle.toLowerCase();
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let itemSize = '';
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// Extract size from missing product
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if (itemTitle.includes('large') && !itemTitle.includes('small')) {
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itemSize = 'large';
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} else if (itemTitle.includes('small') && !itemTitle.includes('large')) {
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itemSize = 'small';
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}
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const cleanItemTitle = missingProductTitle
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.replace(/Colour:\s*[^,\s-]+(?:\s+[^,\s-]+)*/i, '')
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.replace(/Size:\s*[^,\s-]+/i, '')
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.replace(/3D-Printed/i, '')
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.replace(/\s+/g, ' ')
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.trim()
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.toLowerCase();
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for (const product of existingProducts) {
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const productTitle = product.title.toLowerCase();
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let productSize = '';
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if (productTitle.includes('large') && !productTitle.includes('small')) {
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productSize = 'large';
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} else if (productTitle.includes('small') && !productTitle.includes('large')) {
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productSize = 'small';
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}
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const cleanProductTitle = product.title
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.replace(/Colour:\s*[^,\s-]+(?:\s+[^,\s-]+)*/i, '')
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.replace(/Size:\s*[^,\s-]+/i, '')
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.replace(/3D-Printed/i, '')
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.replace(/\s+/g, ' ')
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.trim()
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.toLowerCase();
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const titleSimilarity = calculateTitleSimilarity(cleanItemTitle, cleanProductTitle);
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let reason = '';
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let confidence = titleSimilarity;
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// Check if it's the same product family but different size
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if (titleSimilarity > 0.8) {
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if (itemSize && productSize && itemSize !== productSize) {
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reason = `Same product, different size (${productSize} vs ${itemSize})`;
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confidence = 0.95; // Very high confidence - likely meant to be this size variant
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} else if (itemSize && !productSize) {
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reason = `Same product, missing size specification`;
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confidence = 0.85;
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} else if (!itemSize && productSize) {
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reason = `Same product, has size: ${productSize}`;
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confidence = 0.8;
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} else {
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reason = `Very similar product`;
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confidence = titleSimilarity;
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}
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} else if (titleSimilarity > 0.6) {
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reason = `Similar product`;
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confidence = titleSimilarity;
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}
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if (confidence > 0.6) {
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suggestions.push({
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product,
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confidence,
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reason
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});
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}
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}
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// Sort by confidence and return top suggestions
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return suggestions
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.sort((a, b) => b.confidence - a.confidence)
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.slice(0, maxSuggestions);
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};
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/**
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* Calculate Levenshtein distance between two strings
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* Used for fuzzy string matching
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