🤖 AI / ML

Attention Mechanism

attention · self-attention · self attention · multi-head attention · scaled dot-product attention · attention layer

Definition

Attention is an operation in neural networks that computes a weighted sum of value vectors from the pairwise similarity between a query and a set of keys, letting a model focus dynamically on the relevant parts of its input.

How the attention mechanism works

Attention operates on three matrices computed from the input vectors: Query (Q), Key (K) and
Value (V). For each token, the similarity between its Query and the Keys of every other token is
computed — those are the attention weights. A weighted sum of the Values gives the token a
context-enriched representation.

The scaled dot-product attention formula:

Attention(Q, K, V) = softmax(Q * K^T / sqrt(d_k)) * V

Dividing by the square root of d_k prevents vanishing gradients at high dimensionality.

Why it matters

Before attention, neural networks processed sequences step by step, each token passing information
to the next. On longer texts the context blurred: by the end of a sentence the model barely
remembered its beginning.

Attention let every token see every other token in the sequence directly. That brought three
fundamental improvements:

Long-range dependencies — the model weighs tokens at the start and the end of a sequence
equally well.
Parallelism — the whole weight matrix is computed in one step, which trains far more
efficiently on a GPU than an RNN does.
Interpretability — attention weights can be visualised, showing what the model is looking at
while processing a given token.

Multi-head attention

Parameter	Value
Number of heads (standard)	8–16
What each head learns	Different aspects: syntax, semantics, coreference
Output	Concatenation of the heads, then a linear projection

Heads specialise: one tracks syntactic dependencies, another semantic similarity, a third positional
patterns.

Use in recommendations and search

In recommender systems, attention is used to model session behaviour: the model weighs which of the
previously viewed products are most relevant for predicting the next one. Unlike a simple average of
embeddings, attention takes the order and the context of interactions into account.

Important: attention is a computationally expensive operation. Inference on transformer models
takes significant resources, which affects the latency of real-time recommendation APIs.

Frequently asked questions

How does self-attention differ from ordinary attention?

In ordinary attention the query and the keys come from different sources, such as an encoder and a decoder. In self-attention the query, keys and values all come from the same sequence. That lets every token look at any other token in the same sequence and build context-dependent representations.

What is multi-head attention?

Several parallel attention mechanisms with different projection matrices. Each head learns to attend to a different aspect — syntactic, semantic, positional. Their outputs are concatenated and projected into the final representation. A standard transformer uses 8 to 16 heads.

What is the computational complexity of attention?

It is O(n squared times d) in the number of tokens n, which becomes a bottleneck on long sequences. That is exactly why LLMs have a context window limit. Modern optimisations such as FlashAttention, sparse attention and sliding-window attention reduce that cost.

How does attention improve e-commerce search?

A transformer with attention understands compound queries such as a blue jacket, not a down jacket, under $60, size M, taking every attribute into account at once. RNN models processed such queries sequentially and lost context on longer phrases.