Product Recommender

Intelligent product recommendation engine for retail digital employees. Recommends products based on customer needs, budget, recipient, occasion, preferences, and purchase history. Supports gift recommendations, outfit pairing, cross-sell, upsell, and "help me decide" flows. Use when someone asks: 推荐, 适合, 送礼, 哪个好, 帮我选, 比较一下, 搭配什么, 有什么好的, recommend, what should I get, best for, gift idea, help me pick, what goes with, suitable for, I'm looking for.

Overview

This skill handles all "help me choose" queries. It goes beyond listing products — it understands the customer's situation, filters intelligently, and presents a curated shortlist with reasons.

Depends on: products[] in knowledge base (Step 03). Works better with: inventory data (to exclude out-of-stock items).

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Intent Extraction

Before recommending, extract these signals from the conversation:

Signal	Examples	How to Extract
Budget	"500以内", "¥200左右", "不超过1000"	Parse number + direction
Recipient	"送妈妈", "给男朋友", "自用"	Named or implied
Occasion	"生日", "面试", "日常穿", "夏天用"	Event or context
Preferences	"素色", "轻便", "不要太甜", "简约风"	Style/attribute keywords
Age/Gender	"30岁女性", "老年人", "男生"	Demographic
Constraints	"不含酒精", "纯棉", "防水"	Hard requirements
Quantity	"买一套", "各来一个"	Number intent

If critical signals are missing (especially budget), ask one clarifying question. Never ask for all missing fields at once.

Reference: intent-extraction.md

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Filtering Logic

Apply filters in this order (hard → soft):

1. Hard filters (eliminate if not met):

   • Budget: price ≤ budget_max (or sale_price if active)
   • Hard constraints: attribute must match (e.g., "纯棉" → filter by material tag)
   • Stock: exclude if stock_qty == 0 (when inventory data available)

2. Soft scoring (rank what remains):

   • Recipient match: suitable_for overlap with recipient description
   • Occasion match: tags overlap with occasion keywords
   • Style/preference match: description + tags keyword overlap
   • Popularity signal: use sales_rank if available, else recency

3. Return top N (default: 3, configurable via max_recommendations)

Reference: filtering-logic.md

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Recommendation Presentation

Standard format (3 recommendations)

text

为您推荐 3 款最适合的选择：

1️⃣ [产品名] ¥[price]
   [1句话说明为什么适合这个场景/人群]
   [关键亮点：1-2个最相关的属性]

2️⃣ [产品名] ¥[price]
   [...]

3️⃣ [产品名] ¥[price]
   [...]

[可选] 您更倾向哪款？我可以帮您查一下库存～

Gift recommendation (add wrapping note)

text

送礼推荐：[产品名] ¥[price]
[为什么适合作为礼物 — 1句话]
[礼盒包装是否可用 if known]

Upsell (when appropriate)

If the customer's budget allows 20% more for a meaningfully better option:

"还有一款 ¥[price+] 的[产品名]，多了[key upgrade]，性价比也很高，要不要看看？" Only suggest once per conversation. Never push if customer declines.

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Special Flows

"帮我比较" (Comparison)

When customer names 2+ specific products:

• Fetch both from KB
• Build a comparison table: price / key specs / suitable for / verdict
• Give a clear recommendation, not just data

"搭配什么" (Outfit/Pairing)

When customer asks what goes with a product:

• Identify the anchor product
• Filter KB for complementary items (matching category tags: "搭配", "配套")
• Present as a complete set with total price

"再便宜一点" (Price objection)

When customer asks for cheaper options after seeing recommendations:

• Re-filter with lower budget
• If nothing cheaper: explain value at current price, don't apologize for price

"没有我想要的" (No match)

When no product passes the filters:

1. Tell the customer honestly
2. Suggest the closest available option
3. Offer to notify when matching product arrives (log as feature request)

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Script

Use scripts/recommend.py for deterministic filtering and scoring. Reference: filtering-logic.md