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recommendation-system-designer

Design recommendation systems from the interaction data up: profile users, items, density, cold start, popularity concentration (Gini, long-tail shares), explicit versus implicit feedback and repeats; choose between popularity, item-item kNN, matrix factorisation (ALS, BPR, EASE), content-based, hybrid, two-tower retrieval and learning-to-rank re-ranking; split by time (leave-last-n or global cut-off) instead of randomly; and evaluate top-k lists with precision, recall, hit rate, MRR, MAP and NDCG plus coverage, novelty, popularity bias, personalisation and diversity against a popularity baseline that is always computed. Covers candidate-generation and ranking architecture, cold-start strategies, business rules, online A/B metrics and feedback loops. Use when someone plans a recommender, 'customers also bought' or 'you may like' features, chooses a recommendation algorithm, or needs to evaluate recommendations offline.

AI & Machine Learning / Classical Machine Learningv1.0.0Updated OCT 25 20263 references3 scripts

Recommenders often fail for design reasons rather than algorithm reasons:

  • a random split that leaks the future
  • no popularity baseline, so a "good" model may only recommend bestsellers
  • RMSE optimised when the product shows a ranked list
  • no plan for new users and items
  • offline metrics that never meet the business metric

This skill sets up the design, the protocol and the evaluation:

  • scripts/interaction_profile.py reads the log and recommends an approach
  • scripts/temporal_split.py splits the log the way the system will be used
  • scripts/rec_eval.py scores any recommendation lists against held-out interactions, with beyond-accuracy metrics and a popularity baseline

Trigger Conditions

Activate this skill when the user:

  • Plans a recommender ("you may also like", "customers also bought", next item, personalised feed or email)
  • Must choose an approach (popularity, item-kNN, ALS, BPR, content-based, two-tower, LTR)
  • Needs an offline evaluation protocol or metrics for recommendations
  • Sees a recommender that only shows popular items, or offline gains that do not show up in A/B tests

Building item-kNN and matrix factorisation models: collaborative-filtering-implementer. Re-ranking with features: learning-to-rank-model-builder.

Workflow

text
1. PROFILE   -> python scripts/interaction_profile.py interactions.csv
2. DEFINE    -> the surface, what counts as relevant (purchase, rating >= 4, watch > 50%), k, constraints
3. SPLIT     -> python scripts/temporal_split.py interactions.csv --mode leave-last --n 2 --prefix data
4. BASELINE  -> popularity is computed automatically by rec_eval.py; add recent-popularity / item-kNN
5. MODEL     -> candidates (CF, content) -> optional re-ranker; write recs.csv (user_id,item_id,rank[,model])
6. EVALUATE  -> python scripts/rec_eval.py --recs recs.csv --test data_test.csv --train data_train.csv --k 10 --min-rating 4
7. ONLINE    -> A/B test on the business metric; monitor coverage and feedback loops

Choosing an Approach

SituationStart withAdd
Little data, strong bestsellerspopularity (overall, per segment, recent)item-kNN
Thousands of users and items, implicit feedbackitem-kNN, ALS / EASEBPR, a re-ranker
Explicit ratingsitem-kNN / MF on ratings, evaluated as a rankingtreat low ratings as negative
Many new items (news, marketplace)content-based (text / attribute embeddings)hybrid with CF when interactions arrive
Many new userspopularity / contextual (location, device, referrer)session-based models
Session data without user idsitem-to-item co-occurrence, session kNN, GRU4Rec / SASRec
Large scale with rich featurestwo-tower retrieval + LTR re-ranking (GBDT LambdaMART)
Repeat consumption (grocery, music)personal frequency/recency baselineCF for discovery

Approach details, trade-offs and cold start: approaches.md.

Evaluation Protocol

  • Split by time. Use leave-last-n per user (common in research) or a global cut-off (closest to deployment). Random splits put the future in train.
  • Filter seen items unless re-recommendation is allowed. rec_eval.py does this by default.
  • Relevance definition: explicit rating >= 4 (--min-rating 4), implicit purchases or long views. Write it down.
  • Always compare with popularity. On the fixture, popularity reached NDCG@10 0.164, a genre-aware list 0.440 and random 0.054. The exit code is 1 when no model beats popularity.
  • Beyond accuracy:
MetricMeasuresWatch for
coverageshare of the catalogue recommendedbelow 20%: long tail ignored
noveltymean self-information of recommended itemsvery low: only bestsellers
popularity biaspopularity of recommended items vs the catalogue averagewell above 1: popularity amplification
personalisation1 - mean Jaccard between users' listsnear 0: everyone gets the same list
diversitydistinct categories per list (with --item-features)single-category lists
  • Segment the results: cold vs warm users, head vs tail items, and new vs returning users.

Metric definitions, sampled-metric pitfalls and offline-online gaps: evaluating-recommenders.md. Verified: precision, recall, MAP and NDCG match an independent computation (NDCG via sklearn.metrics.ndcg_score).

Architecture

text
candidate generation (CF, content, popularity, rules)  ->  ~500 candidates per user
ranking (GBDT / neural re-ranker with user, item, context features)  ->  scored list
business logic (availability, diversity, dedup, fairness to sellers, exploration)  ->  final k
logging (impressions, clicks, position) -> training data and evaluation

Serving patterns, feedback loops, exploration and A/B testing: production-recommenders.md.

Output Format

When designing a recommender, deliver:

  1. Data profile - size, density, cold start, popularity concentration, feedback type.
  2. Problem definition - surface, relevance, k, constraints, business metric.
  3. Approach - baselines, candidate sources, ranker, cold-start fallback.
  4. Offline evaluation - split, the metric table against popularity, segment results.
  5. Online plan - A/B design, guardrails (coverage, diversity), monitoring.

Scripts

bash
python scripts/interaction_profile.py ratings.csv
python scripts/interaction_profile.py events.csv --user-col visitor --item-col product --time-col ts --cold 5 --json

Exit codes: 0 profiled, 2 usage or read error.

bash
python scripts/temporal_split.py ratings.csv --mode leave-last --n 2 --prefix ratings
python scripts/temporal_split.py events.csv --mode global --test-frac 0.2 --time-col ts --prefix events

Writes <prefix>_train.csv and <prefix>_test.csv, and reports cold test users and unseen test items. Exit codes: 0 written, 2 usage or read error.

bash
python scripts/rec_eval.py --recs recs.csv --test test.csv --train train.csv --k 10 --min-rating 4 --item-features items.csv
python scripts/rec_eval.py --recs all_models.csv --test test.csv --train train.csv --k 5 20 --json

Exit codes: 0 the best model beats popularity, 1 no model beats popularity, 2 usage or read error.

Additional Resources

FileContent
approaches.mdpopularity, neighbourhood, MF, EASE, content, hybrid, sequential, two-tower, LTR; cold start
evaluating-recommenders.mdsplits, metric definitions, sampled metrics, segment analysis, offline vs online
production-recommenders.mdmulti-stage architecture, serving, business rules, exploration, feedback loops, A/B tests
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Implement collaborative filtering recommenders that beat popularity: item-based kNN with cosine, Jaccard or asymmetric similarity, shrinkage, neighbourhood size, positive-only or mean-centred rating signals and 'because you liked X' explanations; implicit ALS with confidence weighting, BPR pairwise ranking and the closed-form EASE model in plain NumPy, tuned on a temporal validation split by recall@k and NDCG@k and refit on all data; plus user-kNN, explicit matrix factorisation, the implicit/LightFM libraries, hyperparameter ranges, cold-start fallbacks, scaling with sparse matrices and approximate nearest neighbours. Produces top-k lists in the format of the recommendation evaluator.