Sequence-Aware Recommendation

Captures the temporal order of user interactions to predict the next likely request. Unlike collaborative filtering, which treats interactions as an unordered set, this mechanism processes a chronologically ordered sequence of content IDs.

• RNN-based encoders: Use hidden states (e.g., GRU, LSTM) to maintain a compressed memory of the interaction history.
• Transformer-based encoders: Employ causal self-attention to weigh the relevance of all prior interactions when predicting the next item.
• Example: A user streaming episodes S01E01 → S01E02 → S01E03 generates a strong sequential signal for pre-caching S01E04 at the edge node.

Uses the Transformer architecture to dynamically assign importance scores to each past interaction in a sequence, regardless of its temporal distance. This overcomes the vanishing gradient problem inherent in basic RNNs.

• Scaled dot-product attention: Computes pairwise relevance between all positions in the sequence.
• Positional encoding: Injects explicit temporal order information since self-attention is permutation-invariant by default.
• Example: A user who watched a movie trailer 3 months ago and now watches a related genre film—the model links these distant events to recommend the full movie for proactive caching.

Distinguishes between modeling a single, anonymous interaction session and modeling a long-term user profile with identifiable session boundaries.

• Session-Based Recommendation (SBRS): Operates on short, anonymous interaction sequences (e.g., a single browsing session). Uses architectures like GRU4Rec to predict the next click without a persistent user ID.
• Session-Aware Recommendation: Incorporates a user's entire historical sequence, partitioned into distinct sessions. This enables the model to learn both short-term intent drift and long-term preference stability.
• Example: An anonymous user's current news reading session triggers caching of related articles; a known user's multi-session history triggers pre-fetching of a weekly podcast series.

Converts discrete content IDs (e.g., video IDs, article slugs) into dense, continuous vector representations that encode semantic and collaborative relationships.

• Content embedding matrix: A learned lookup table where each row represents a unique item in the catalog.
• Feature-rich embeddings: Augment item IDs with metadata (genre, duration, creator) to improve cold-start handling for new content.
• Example: Two distinct video IDs for a movie trailer and the full movie can be embedded close together in vector space, allowing the model to generalize the sequential transition from trailer to feature.

The model is trained as a sequence classification task, where the goal is to predict the item at the next time step given the preceding sequence.

• Categorical cross-entropy loss: The model outputs a probability distribution over the entire item catalog, and loss is computed against the ground-truth next item.
• Negative sampling: Efficiently approximates the full softmax over millions of items by only updating a small subset of negative examples during training.
• Teacher forcing: During training, the ground-truth previous item is fed as input, not the model's own prediction, to stabilize convergence.
• Example: Given the sequence [A, B, C], the model is penalized if it assigns a low probability to the actual next item D, while being trained to assign low probabilities to all other items.

The sequence-aware model's output—a ranked list of predicted next-content IDs—is consumed by the proactive caching orchestration layer to trigger pre-fetching.

• Inference latency budget: Model inference must complete within the user's content consumption window (e.g., during video playback) to hide pre-fetching latency.
• Confidence thresholding: Only predictions exceeding a calibrated probability threshold trigger a cache warming action to avoid wasting backhaul bandwidth on low-confidence guesses.
• Cache invalidation hook: If a user's actual next request diverges from the prediction, the pre-fetched content's priority in the cache eviction policy is immediately downgraded.
• Example: A sequence-aware model running on the RAN Intelligent Controller (RIC) predicts the next video segment and signals the MEC Caching node to pre-fetch it via QUIC (0-RTT).