You are designing a deep-learning–based recommendation system that uses a Transformer-style **cross-attention** block to model the interaction between a user and a candidate item. The model has these typical inputs: - A **user behavior sequence**: a list of items the user has interacted with in the past, each already embedded as a vector (e.g., size `d`). - A **candidate item** whose relevance score you want to predict, also embedded as a vector of size `d`. - Optional **context features** (time, device, location, etc.) that can also be embedded. You decide to use a cross-attention layer somewhere in the model rather than only self-attention. 1. Propose a concrete way to define the **Query (Q)**, **Key (K)**, and **Value (V)** tensors in this cross-attention block using the inputs above. Explain what each of Q, K, and V represents semantically. 2. Give at least **two different reasonable design choices** for how to set up Q, K, and V (for example, one where the candidate item is the query and one where the user history is the query). For each design, explain: - What is used as Q, K, and V. - What interaction the attention mechanism is modeling. - Pros and cons or when that design is preferable. 3. Briefly explain how cross-attention here differs from self-attention within the user behavior sequence, and why cross-attention can be useful in recommendation systems.