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