sequential recommendation

**Sequential recommendation** considers **the order and timing of user interactions** — modeling how preferences evolve over time and predicting next items based on interaction sequences, capturing temporal dynamics that static models miss. **What Is Sequential Recommendation?** - **Definition**: Recommend based on ordered sequence of past interactions. - **Input**: Time-ordered user history (item1 → item2 → item3 → ...). - **Output**: Next items user likely to interact with. - **Goal**: Capture temporal patterns, evolving preferences. **Why Sequence Matters?** - **Temporal Patterns**: Preferences change over time. - **Context**: Recent items more relevant than old ones. - **Intent**: Sequence reveals user goals (browsing → comparing → buying). - **Seasonality**: Holiday shopping, back-to-school, summer trends. **Techniques** **Markov Chains**: Model item-to-item transitions. **RNNs/LSTMs**: Learn long-term sequential dependencies. **Transformers**: Self-attention over interaction history (BERT4Rec, SASRec). **Temporal CNNs**: Convolutional networks over time. **Memory Networks**: Explicitly model short and long-term memory. **Applications**: E-commerce (next purchase), streaming (next video/song), news (next article), social media (next post). **Challenges**: Long sequences, concept drift, computational cost, cold start. **Tools**: RecBole, TensorFlow Recommenders, SASRec, BERT4Rec, GRU4Rec.