algo-rec-session

# 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 |...