---

name: "algo-rec-session" description: "Implement session-based recommendation from short-term user behavior sequences without long-term profiles. Use this skill when the user needs to recommend in anonymous sessions, predict next click from browsing sequence, or build recommendations for non-logged-in users — even if they say 'what should they click next', 'anonymous user recommendations', or 'browsing sequence prediction'." metadata: category: "WP-36 推薦系統" tags: ["recommendation", "session-based", "sequential", "real-time"]

Session-Based Recommendation

Overview

Session-based recommendation predicts the next item a user will interact with based on their current session's click/view sequence, without relying on long-term user profiles. Uses Markov chains, association rules, or neural approaches (GRU4Rec). Operates in real-time with O(sequence_length) inference.

When to Use

Trigger conditions:

• Anonymous users (no login, no long-term profile)
• Short browsing sessions where recency matters most
• Real-time "next item" prediction during active sessions

When NOT to use:

• When rich user history is available (use CF or content-based for better personalization)
• When sessions are extremely short (1-2 clicks) — insufficient signal

Algorithm

IRON LAW: First Few Clicks Are Disproportionately Important
Session-based methods operate WITHOUT long-term profiles. Intent must
be inferred from SHORT sequences. The first 2-3 clicks establish the
session's intent — misreading early signals derails the entire session.

Phase 1: Input Validation

Parse clickstream into sessions (by session ID or timeout-based splitting, typically 30min inactivity). Filter sessions below minimum length (3+ events). Gate: Sessions parsed, minimum length threshold applied.

Phase 2: Core Algorithm

Markov Chain approach:

1. Build transition matrix from item-to-item sequences across all sessions
2. For current session [A, B, C], predict next item from P(next | C) or higher-order P(next | B, C)

Association Rules approach:

1. Mine frequent item sequences (sequential pattern mining)
2. Match current session suffix against known patterns
3. Recommend items that frequently follow the matched pattern

Phase 3: Verification

Evaluate with leave-one-out: hide last item in each session, predict, check hit rate and MRR (Mean Reciprocal Rank). Gate: Hit@20 significantly above random baseline.

Phase 4: Output

Return ranked next-item predictions with confidence scores.

Output Format

{
  "predictions": [{"item_id": "789", "score": 0.65, "based_on": "last_3_clicks"}],
  "session": {"length": 5, "items_viewed": ["a", "b", "c", "d", "e"]},
  "metadata": {"method": "markov_order2", "hit_rate_at_20": 0.35}
}

Examples

Sample I/O

Input: Session: [shoes_page, running_shoes, nike_air_max] Expected: Recommend: nike_air_zoom (0.72), adidas_ultraboost (0.58), shoe_size_guide (0.41)

Edge Cases

Input	Expected	Why
Session length = 1	Popularity fallback	Single click insufficient for sequence pattern
Repeated item views	Weight recency, not count	User may be comparing, not broadening
Session intent shift	Adapt to latest clicks	User changed their goal mid-session

Gotchas

• Session definition matters: 30-minute timeout is conventional but arbitrary. E-commerce may need shorter (15min); research browsing may need longer (60min).
• Position bias: Users click top results more. Session data reflects UI position, not just preference. Correct for position bias.
• Repeat recommendations: Users often revisit items. Distinguish "recommend something new" from "remind of previously viewed."
• Cold start for new items: Items with zero prior session appearances can't be predicted by transition matrices. Mix in feature-based candidates.
• Computational efficiency: For real-time inference, pre-compute transition probabilities. Recomputing per-request at scale is too slow.

References

• For GRU4Rec neural session model, see references/gru4rec.md
• For session splitting heuristics, see references/session-splitting.md