Book Recommendations & Reader Segments

Nine recommenders benchmarked on the same split, five behavioural reader segments, and a model that predicts how much someone will read next quarter.

Model leaderboard

One split, one protocol, every model. RMSE on held-out ratings; HR and NDCG against 99 popularity-matched negatives.

Accuracy against catalogue coverage

The trade nobody escapes: the models that rank best recommend from a narrow slice of the catalogue.

The long tail

Share of all ratings held by the most-read share of titles.

Rating distribution

Readers rate generously — the ceiling is real and every RMSE below lives with it.

Finish rate by rating

The implicit signal agrees with the explicit one.

Reader segments

k-means on 17 behavioural features, projected to two principal components.

Segment profiles

Named from each cluster's own centroid, not from the held-out label.

Engagement model — will they keep reading?

What the engagement model leans on

Permutation importance on the held-out users.

Cold start

Readers with fewer than 10 ratings in training. NDCG@10, same protocol.

Sample recommendations

Top five from the hybrid, for six held-out readers.