Books-specific framing hides the category-generic design #15

Problem

The pipeline is category-generic — AMAZON_CATEGORIES in holmes/data/preprocess.py enumerates ~30 Amazon Reviews 2023 categories (All_Beauty, Electronics, Video_Games, …), preprocess --all fans out over every one into data/processed/<category>/, and build_dataset accepts any category by name. Books is only the default --category, not the domain.

But the project's own materials narrate it as a Books-specific system, which misleads readers into thinking there is a single domain:

• CLAUDE.md calls it an "ALS book recommender (Amazon Reviews 2023, Books)".
• holmes/data/preprocess.py:1 module docstring: "Build a sparse user-item interaction matrix from Amazon Reviews 2023 (Books)", as if Books were the domain.
• tests/conftest.py names the shared fixture books_dataset and frames it in book/customer terms — yet it models a generic grouped user×item interaction matrix with nothing book-specific about it.

Three independent artifacts encode the default as if it were the design. This is drift: the docs/naming under-describe the system's generality.

Proposed fix

• Rename the books_dataset fixture to something category-neutral (e.g. grouped_dataset / interaction_dataset) across tests/ — used in conftest.py, test_dataset.py, test_harness.py, test_als.py, test_diagnostics.py.
• Correct the preprocess.py module docstring to describe the matrix as built from any Amazon Reviews 2023 category (Books being the default), not Books specifically.
• Update the CLAUDE.md "What this project is" framing from "book recommender" to a category-generic Amazon Reviews recommender.
• (Optional) Align the fixture's "customer" vocabulary with the Dataset class's "user" naming.

Why it matters

The framing is misleading enough that a reader working only from the project's docs concludes it is single-domain. Naming/docs should reflect that the system is category-generic.