Session-Based Recommendation
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.