🎯 RecSys Orbit

⭐ Rating

📊 Matrix

🔍 Similarity

🎯 Prediction

🎯 Recommender Systems

Collaborative Filtering • Matrix Factorization • Deep Learning • Session-Based • Context-Aware

Master the algorithms that power personalization — from collaborative filtering and matrix factorization to deep learning-based recommendations and real-time personalization systems.

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Recommender Systems Concepts & Algorithms

Recommender Basics

Types, approaches, evaluation, challenges.

Evaluation Metrics

RMSE, MAE, Precision, Recall, NDCG, MAP.

User-Based CF

Neighborhood, similarity, Pearson, cosine.

Item-Based CF

Item similarity, slope one, scalability.

k-NN for Recommendations

Nearest neighbors, distance metrics.

Model-Based CF

Regression, Bayesian, clustering.

Matrix Factorization

SVD, latent factors, embeddings.

Alternating Least Squares

ALS, implicit feedback, scalability.

SVD++

Implicit feedback integration.

Factorization Machines

Feature interactions, regression, ranking.

Field-Aware FM

FFM, CTR prediction, Criteo.

Neural Collaborative Filtering

NCF, embeddings, MLP, GMF.

Wide & Deep

Memorization + generalization.

DeepFM

FM + deep neural networks.

Deep Interest Network

Attention, user behavior, CTR.

Transformer for Rec

BERT, SASRec, XLNet, sequential.

Session-Based Recommendations

GRU4Rec, NARM, STAMP.

SASRec

Self-attention, sequential, session.

Graph-Based Session Rec

SR-GNN, session graphs, attention.

Context-Aware Rec

Time, location, weather, context.

Temporal Recommendations

Time-aware, decay, seasonality.

Hybrid Recommendations

Weighted, switching, cascade.

Ensemble Methods

Stacking, boosting, blending.

Content-Based Recommendations

TF-IDF, vectors, similarity.

Deep Learning for Content

CNNs, embeddings, text/vision.

Knowledge Graph Rec

KG embedding, RippleNet, KGCN.

KG + Transformer

KG-BERT, KGT, graph attention.

Cold Start Problem

User cold start, item cold start, solutions.

Active Learning for Rec

Uncertainty, diversity, exploration.

Learning to Rank

Pointwise, pairwise, listwise.

Bayesian Personalized Ranking

BPR-MF, pairwise ranking.

LambdaMART

GBDT, RankNet, LambdaRank.

Fairness in RecSys

Bias, fairness metrics, debiasing.

Debiasing Methods

Propensity, exposure, IPS.

RecSys Architecture

Retrieval, ranking, serving.

Real-Time Recommendations

Streaming, online learning.

A/B Testing for RecSys

Experimentation, metrics, analysis.

Surprise Library

CF, SVD, ALS, evaluation.

Implicit Library

ALS, BPR, implicit feedback.

RecBole Framework

Benchmarks, models, datasets.

E-Commerce Rec

Product, basket, cross-sell.

Media & Entertainment

Movies, music, articles.

News Recommendations

Recency, diversity, engagement.

🎯 Recommender Systems Mastery Roadmap

🔰 Foundation

Basics, CF, similarity, evaluation

📶 Core

MF, ALS, content-based, session-based, ranking

🔥 Deep Dive

Neural CF, DL models, KG, fairness, cold start

🏆 Frontier

Transformers, graph-based, debiasing, real-time systems

🎯 Personalize, Predict, Engage — Master Recommender Systems

From collaborative filtering to deep learning, build systems that understand and delight users.

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