Product Recommender#

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:

SignalExamplesHow to ExtractBudget"500以内", "¥200左右", "不超过1000"Parse number + directionRecipient"送妈妈", "给男朋友", "自用"Named or impliedOccasion"生日", "面试", "日常穿", "夏天用"Event or contextPreferences"素色", "轻便", "不要太甜", "简约风"Style/attribute keywordsAge/Gender"30岁女性", "老年人", "男生"DemographicConstraints"不含酒精", "纯棉", "防水"Hard requirementsQuantity"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