Session-based recommendation predicts what users want next within a browsing session — analyzing current session behavior (clicks, views, searches) to recommend items in real-time, without requiring user accounts or long-term history, ideal for e-commerce and anonymous browsing.

What Is Session-Based Recommendation?

• Definition: Recommend based on current session activity.
• Input: Sequence of items viewed/clicked in current session.
• Output: Next items user likely to interact with.
• Goal: Capture short-term intent, immediate needs.

Why Session-Based?

• Anonymous Users: No login required, works for guests.
• Immediate Intent: Capture what user wants right now.
• E-Commerce: Shopping sessions have clear goals.
• Privacy: No long-term tracking needed.
• Real-Time: Adapt to user behavior instantly.

Techniques

Markov Chains: Predict next item from current item transition probabilities. Recurrent Neural Networks: LSTMs, GRUs learn session sequences. Transformers: Self-attention over session items (BERT4Rec, SASRec). Graph Neural Networks: Model item-to-item transitions as graph.

Session Features: Item sequence, dwell time, clicks, add-to-cart, searches, filters applied.

Applications: E-commerce (Amazon, eBay), news (Google News), video (YouTube), music (Spotify).

Challenges: Short sessions, noisy signals, cold start for first item, session boundaries.

Tools: TensorFlow Recommenders, RecBole, GRU4Rec, BERT4Rec implementations.