Recommendation & search
Information retrieval
site search, document search, semantic search
Return the most relevant documents for a query, within a latency budget.
Linear algebra
Probability & Bayes
Information theory
Foundations
TF-IDF and BM25 come from information theory and probabilistic relevance; dense retrieval lives in an inner-product space.
Frame the problem
Frame
Define relevance: clicks, judgments, or both. The latency budget decides the architecture.
Labeling & data collection
Data & labels
Click logs carry position bias. Graded judgments are expensive. Use both.
Modalities → vectors
Tokenization
word2vec & GloVebackground
Represent
Tokenisation and analysers for the lexical side; a text encoder for the dense side.
Split the data
Split
By query, never by document, or the ranker memorises documents.
Dumb baseline
TF-IDF + linear
BM25 & lexical search
Dense retrieval
Bi-encoder plus an ANN index. Train with in-batch negatives and hard negatives mined from BM25.
Metric learning
Two-tower retrieval & ANN
Hybrid
Union of lexical and dense candidates.
The funnel & cold start
Re-rank
LambdaMART on features, or a cross-encoder over the top k.
Learning to rank
Bi- vs cross-encoders
Model
BM25 is hard to beat. Ship it first.
Fine-tuning a pretrained model
Loss functions
Train
Contrastive loss for the bi-encoder; listwise for the ranker.
Ranking & recsys metrics
Embedding geometry & retrieval eval
Evaluate offline
Recall@k for retrieval, NDCG for the ranker. Sanity-check embedding geometry.
A/B testing & interleaving
Evaluate online
Interleaving.
Serving & release
Inference optimisation
Ship & monitor
Index refresh cadence. ANN recall against latency.
Press enter or space to select a node. You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.
Press enter or space to select an edge. You can then press delete to remove it or escape to cancel.