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).

Intent Extraction

Before recommending, extract these signals from the conversation:

If critical signals are missing (especially budget), ask one clarifying question.

Never ask for all missing fields at once.

Reference: intent-extraction.md

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)

1. 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

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

Reference: filtering-logic.md

Recommendation Presentation

Standard format (3 recommendations)

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)

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.

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)

Script

Use scripts/recommend.py for deterministic filtering and scoring.

Reference: filtering-logic.md