Öneri Sistemleri

Why Do Recommender Systems Matter? Birth, Present, and Future of a Discipline

Recommender engines are the one engineering discipline shaping the internet: 80% of Netflix watching, 70% of YouTube consumption, 35% of Amazon revenue come from recommenders. We see the birth, billion-dollar impact, and why now is the moment.

Who Is a Recommender Engineer? Skill Atlas and Junior → Staff Career Map

Recommender Engineer = a specific intersection of Data Engineer + ML Engineer + ML Researcher + Backend Engineer. Full atlas across 8 skill categories, junior → senior → staff path, typical interview questions, and T-shaped specialization strategy.

Course Philosophy: Math → Manual Code → Library → Benchmark → Production

Why is this course different from a typical 'Coursera style'? We approach every topic in 5 stages: (1) Pen-and-paper math, (2) From-scratch NumPy, (3) Production-style library, (4) Benchmark on the same dataset, (5) 'Production gotcha' note. Why does this order give 3x deeper learning?

Workshop Setup: Python 3.12, uv, PyTorch, FAISS, Polars and Jupyter Lab

We set up a modern Python environment for recommender systems work from scratch: uv (Rust-based, 80x faster than conda), Python 3.12, PyTorch 2.5+, FAISS CPU+GPU, Polars, implicit, lightfm, surprise, Jupyter Lab. Mac, Windows (WSL2), Linux, and Google Colab options.

Our Datasets and the Ethics Contract: From MovieLens to H&M, Going to the Field

Full profile of 8 datasets we'll use: MovieLens (3 sizes), Amazon Reviews (2023), RetailRocket, H&M Fashion, MIND News, Spotify MPD, Last.fm, Yelp. Each one's license, size, download steps, suitability and ethics contract.

Where Do Recommenders Run? An Architecture Tour: Netflix, YouTube, Spotify, Amazon, TikTok, Trendyol

A concrete architecture tour of 6 major companies based on published engineering blogs: Netflix retrieval-ranking, YouTube two-stage, Spotify BaRT, Amazon item-CF heritage, TikTok Monolith, Trendyol personalization.

Problem Typology: Rating Prediction vs. Ranking vs. Top-N Retrieval vs. Sequential

A recommender problem can be formulated in 4 different ways — and choosing the right formulation is often more important than choosing the right algorithm. Each one's mathematical definition, when to choose, which metric, and which real scenarios.

Explicit and Implicit Feedback: A Complete Guide from 1-5 Stars to Click-Skip Behavior

Two fundamental data types in recommender systems: explicit (intentionally given stars/likes) vs implicit (click, dwell time, completion, skip). Differences, loss function impact, bias sources, hybrid usage, real-world labeling strategies.

Three Faces of the Cold-Start Problem: User, Item, System — and Practical Solutions

The most annoying problem in recommenders: how do you recommend for a user/item you have no data on? Strategy map for user cold-start, item cold-start, and system cold-start — from Netflix's 5-film screen to TikTok's viral loop.

MovieLens from Zero: Schema, EDA, and Efficient Loading with Polars

File structure of MovieLens-100K, 1M, 25M, row-by-row schema, lazy/streaming load with Polars (10-30x faster than Pandas), sparse matrix conversion, first EDA graphics, and data quality checks.

Turning Implicit Feedback into Labels: Click, Dwell, and Multi-Signal Aggregation

Raw e-commerce site logs → trainable labeled dataset. Math and NumPy implementation of Hu/Koren confidence weighting, multi-signal weighted aggregation, session reconstruction, preventing label leakage.

Bias Galaxy: Position, Presentation, Popularity, Exposure and IPS Correction

5 important biases in recommender systems (position, presentation, popularity, exposure, selection), each with mathematical definition, ways to observe in log data, and Inverse Propensity Scoring (IPS) correction derivation + NumPy implementation.

GDPR, KVKK and the Right to Be Forgotten: Legal Compliance in Recommenders

How does a recommender system comply with data subject rights (access, deletion, portability)? EU AI Act 2024-2026 timeline, KVKK's 2025 update, removing user data from ML models (machine unlearning), audit log requirements.

Accuracy Metrics: RMSE, MAE, Precision@K, Recall@K, MAP, MRR, NDCG, HR@K — Full Math + NumPy

Full mathematical definition of 8 main accuracy metrics, from-scratch NumPy implementation, comparative run on MovieLens, and which metric to choose when — recommender engineer's metric cheat sheet.

Beyond-Accuracy: Coverage, Diversity (ILS), Novelty, Serendipity, and Popularity Bias Measurement

Why does a recommender with high NDCG but bored users exist? Because only 'accuracy' was measured. Coverage, intra-list similarity (ILS), novelty, serendipity, gini coefficient measure all faces of the system.

Data Splitting Strategies: Random, Time, User, Leave-One-Out — Practical Trade-Offs

How you split MovieLens changes NDCG — 0.15 or 0.25. This lesson covers 5 main split strategies, when each is correct, when each leaks, and a comparison from a production realism standpoint.

Online Evaluation: A/B Test, Interleaving, CUPED and Statistical Power

You raised offline NDCG +2% — verify with A/B test before production deploy that user behavior really changes. A/B test sample size math, interleaving (10x more efficient), CUPED variance reduction, and switchback testing.

The Offline-Online Gap: The Dacrema Crisis and Correct Protocol Selection

In 2019, Dacrema, Cremonesi, Jannach paper shook the recommender literature: 'Are neural recommenders really better? Most are beaten by even classic k-NN.' This lesson covers the reproducibility crisis, the offline-online correlation problem, and how to select the correct protocol.

Content-Based Filtering Philosophy: 'What They Watched' vs 'What It's Like'

Collaborative filtering searches for 'similar users', content-based searches for 'similar items'. This philosophical difference dictates technical decisions — cold-start advantage, filter bubble disadvantage, hybrid strategies. Concept + math + industry positioning.

Item Profiling: TF-IDF, BM25, n-grams, and Categorical Feature Encoding — Math + NumPy

Foundation of content-based recommenders: converting item to numerical vector. Full TF-IDF formula derivation + from-scratch NumPy implementation, BM25 vs TF-IDF difference, n-grams on movie titles, categorical encoding (one-hot, target, frequency).

From-Scratch NumPy Content-Based Recommender: 150 Lines on MovieLens-100K

The backbone lesson of this module: building a real content-based recommender on MovieLens-100K — pure NumPy, 150 lines, end-to-end. Item profiling, user profile vector, cosine scoring, top-N recommendation, evaluation. Then compare with sklearn and the first row in our baseline table.

Production Notes: Feature Drift, Multi-Modal Content, and Challenges of Turkish NLP

Module 4 closing: real problems you'll face after keeping a content-based recommender in production for 6 months. Feature distribution drift, multi-modal embeddings (image+text+audio) for cold-start power, CLIP/SBERT modern approaches, Turkish NLP specifics with stemming + BERTurk.

k-NN Collaborative Filtering: User-User vs Item-Item — When to Use Which?

Recommender's birth paper, GroupLens 1994. After 30 years, still the baseline of every recommender system. This lesson covers philosophical differences between user-user and item-item CF, mathematical formulation of each, and when each wins.

Similarity Metrics: Pearson, Cosine, Adjusted Cosine, Jaccard — Full Math + NumPy

Foundation of all CF algorithms: 4 main similarity metrics. Pearson correlation (rating bias correction), cosine similarity (vector direction), adjusted cosine (user bias correction), Jaccard (binary implicit). Full mathematical derivation + from-scratch NumPy + MovieLens comparison.

From-Scratch Item-Item k-NN with NumPy: Production-Grade on MovieLens-1M

Module 5's backbone: production-grade item-item k-NN from scratch on MovieLens-1M. Adjusted cosine + shrinkage, sparse matrix optimizations, offline batch precomputation pattern, top-K neighbor caching, second row in our benchmark.

Scalability Ceilings: Optimization Strategies Above 100M Ratings

MovieLens-1M is too small — in real world you work with 100M+ ratings, 10M+ items. This lesson: offline batch precomputation pattern, LSH (Locality-Sensitive Hashing), MinHash for approximate Jaccard, distributed computation with MapReduce/Spark, Redis-based serving.