Awesome Search

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I've been building e-commerce search applications for almost ten years. Below you can find a list of (some) publications, conferences and books that inspire me. Grouped by topic (If an article fits into multiple topics - it goes into multiple sections).

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Also check my other collections awesome e-commerce, awesome knowledge graphs, awesome cloud apps

Topics

• General, fun, philosophy
• Types of search
  • Classic/Lexical search
  • Vectors/Semantic search
    • Embeddings
      • Types of vectors
        • Dense vectors
          • Matryoshka embeddings
        • Sparse vectors
          • SPLADE
      • Encoder models
        • Bi-encoders
        • Cross-encoders
      • Handling high-dimension embeddings
        • Dimensionality reduction
        • Quantization
          • Scalar quantization
          • Binary quantization
    • Symmetric and Asymmetric semantic search
  • Hybrid search
  • Multimodal search
• Also types of search
  • Conversational search
• Search Results
  • Retrieval
    • Relevance
      • Relevance Algorithms
  • Ranking
    • Multi stage ranking
    • Learning to Rank
      • Click models for search
  • Bias
  • Diversification
  • Personalisation
  • Zero search results
• Search UX
  • Baymard Institute
  • Nielsen Norman Group
  • Enterprise Knowledge LLC
  • Facets
    • Accidental Taxonomist
  • Other
• Spelling correction
• Suggestions
• Synonyms
• Stopwords
• Graphs/Taxonomies/Knowledge Graph
  • Integrating Search and Knowledge Graphs (by Enterprise Knowledge)
• Query expansion
• Query understanding
  • Search Intent
  • Query segmentation
• Algorithms
  • BERT
  • ColBERT
  • RRF
  • Collocations, common phrases
  • Other Algorithms
• Tracking, profiling, GDPR, Analysis
• Experiments
  • A/B testing, MABs
• Testing, metrics, KPIs
  • Metrics
  • KPIs
  • Evaluating Search (by Daniel Tunkelang)
  • Measuring Search (by James Rubinstein)
  • Three Pillars of Search Relevancy (by Andreas Wagner)
• Architecture
• Vectors search
• Education and networking
  • Conferences
  • Trainings and courses
  • Books
  • Blogs and Portals, News
  • Papers
• Management, Search Team
  • Job Interviews
  • Engineering
• Industry players
  • Personalies and influencers
  • Search Engines
  • Products and services
  • Consulting companies
• Blogposts series
  • Search Optimization 101 (by Charlie Hull)
  • Query Understanding (by Daniel Tunkelang)
  • Grid Dynamics
  • Considering Search: Search Topics (by Derek Sisson)
• Videos
  • Channels
  • Featured
• Case studies
  • General search
  • Multisided markets
  • E-commerce
• Datasets
• Tools

Unsorted

• sandbox Jun 2021
• sandbox May 2021
• sandbox April 2021
• sandbox Dec 2020
• sandbox Jan 2020

General, fun, philosophy

• Falsehoods Programmers Believe About Search
• Ethical Search: Designing an irresistible journey with a positive impact
• On Semantic Search
• Feedback debt: what the segway teaches search teams
• Supporting the Searcher’s Journey: When and How
• Shopping is Hard, Let’s go Searching!
• An Introduction to Search Quality
• On-Site Search Design Patterns for E-Commerce: Schema Structure, Data Driven Ranking & More
• In Search of Recall
• Balance Your Search Budget!

Types of search

• Evolution of Search Technology: A Look Ahead

Classic/Lexical Search

• Etsy. Targeting Broad Queries in Search
• How Etsy Uses Thermodynamics to Help You Search for “Geeky”
• Broad and Ambiguous Search Queries
• Deconstructing E-Commerce Search: The 12 Query Types

Vectors/Semantic search

• Migrating to Elasticsearch with dense vector for Carousell Spotlight search engine

• From zero to semantic search embedding model

• Guidelines to choose an index

• Pinecone Series

  • Nearest Neighbor Indexes for Similarity Search
  • The Missing WHERE Clause in Vector Search

  Symmetric and Asymmetric semantic search

  • Symmetric vs. Asymmetric Semantic Search

Embeddings

Types

• Bi-encoder vs Cross encoder?When to use which one?

Bi-encoders

Cross-encoders

Dense Vectors

Matryoshka embeddings

• Matryoshka embeddings: faster OpenAI vector search using Adaptive Retrieval
• Introduction to Matryoshka Embedding Models
• Matryoshka representations. A guide to faster semantic search

Sparse Vectors

SPLADE

• Hybrid Search: SPLADE (Sparse Encoder)
• SPLADE for Sparse Vector Search Explained

Handling high-dimension embeddings

Dimensionality reduction

Quantization

Binary embeddings

Hybrid search

• Hybrid search > sum of its parts?
• On Hybrid Search
• Hybrid search with Re-ranking

Multimodal search

• Muves: Multimodal & multilingual vector search w/ Hardware Acceleration
• Model Selection for Multimodal Search

Also types of search

Conversational search

• Search as a Conversation
• Affordances for Conversational Search
• Query Understanding and Chatbots

Search Results

Retrieval

Relevance

• Humans Search for Things not for Strings
• What is a ‘Relevant’ Search Result?
• How to Achieve Ecommerce Search Relevance
• Setting up a relevance evaluation program

Relevance Algorithms

• Practical BM25: How Shards Affect Relevance Scoring in Elasticsearch, The BM25 Algorithm and its Variables
• The influence of TF-IDF algorithms in eCommerce search
• BM25 The Next Generation of Lucene Relevance
• Lucene Similarities (BM25, DFR, DFI, IB, LM) Explained

Ranking

Multi-stage ranking

• Multi stage ranking

Learning to Rank

• How is search different than other machine learning problems?
• Reinforcement learning assisted search ranking
• E-commerce Search Re-Ranking as a Reinforcement Learning Problem
• When to use a machine learned vs. score-based search ranker
• What is Learning To Rank?
• Using AI and Machine Learning to Overcome Position Bias within Adobe Stock Search
• Train and Test Sets Split for Evaluating Learning To Rank Models
• How LambdaMART works - optimizing product ranking goals

Click models for search

• Click models
• Click Modeling for eCommerce
• Using Behavioral Data to Improve Search

Bias

• What is Presentation Bias in search?
• Dealing with Position Bias in Recommendations and Search

Diversification

• Search Result Diversification using Causal Language Models
• Learning to Diversify for E-commerce Search with Multi-Armed Bandit
• Search Quality for Discovery & Inspiration
• How to measure Diversity of Search Results
• Searching for Goldilocks
• Broad and Ambiguous Search Queries - Recognizing When Search Results Need Diversification
• Thoughts on Search Result Diversity

Personalisation

• Patterns for Personalization in Recommendations and Search
• Daniel Tunkelang Personalization
• Airbnb - [Real-time personalization in