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.
- General, fun, philosophy
- Types of search
- Search UX
- Spelling correction
- Synonyms
- Suggestions
- BERT
- Spacy
- Word2Vec
- Collocations, common phrases
- Graphs/Taxonomies/Knowledge Graph
- Query understanding
- Learning to rank
- Other, Search
- Other, Algorithms
- Tracking, profiling, GDPR, Analysis
- Testing and metrics
- Conferences
- Books
- Personalies and influencers
- Videos
- Usecases
- Falsehoods Programmers Believe About Search
- Ethical Search: Designing an irresistible journey with a positive impact
- Humans Search for Things not for Strings
- On Semantic Search
- Feedback debt: what the segway teaches search teams
- Supporting the Searcher’s Journey: When and How
- Search: Intent, Not Inventory
- Etsy. Targeting Broad Queries in Search
- How Etsy Uses Thermodynamics to Help You Search for “Geeky”
- Daniel Tunkelang. Broad and Ambiguous Search Queries
- Deconstructing E-Commerce Search: The 12 Query Types
- Autodirect or Guide Users to Matching Category
- 13 Design Patterns for Autocomplete Suggestions (27% Get it Wrong)
- E-Commerce Search Needs to Support Users’ Non-Product Search Queries (15% Don’t)
- Search UX: 6 Essential Elements for ‘No Results’ Pages
- Product Thumbnails Should Dynamically Update to Match the Variation Searched For (54% Don’t)
- Faceted Sorting - A New Method for Sorting Search Results
- The Current State of E-Commerce Search
- E-Commerce Sites Need Multiple of These 5 ‘Search Scope’ Features
- E-Commerce Search Field Design and Its Implications
- E-Commerce Sites Should Include Contextual Search Snippets (96% Get it Wrong)
- E-Commerce Search Usability: Report & Benchmark
- Six ‘COVID-19’ Related E-Commerce UX Improvements to Make
- Peter Norvig. "How to Write a Spelling Corrector". Classic publication.
- Daniel Tunkelang. "Spelling Correction"
- A simple spell checker built from word vectora
- A closer look into the spell correction problem: 1, 2, 3, preDict
- Deep Spelling
- Modeling Spelling Correction for Search at Etsy
- Wolf Garbe. Author of Sympell. 1000x Faster Spelling Correction algorithm, Top highlight SymSpell vs. BK-tree: 100x faster fuzzy string search & spell checking, Fast Word Segmentation of Noisy Text
- Chars2vec: character-based language model for handling real world texts with spelling errors and
- JamSpell, spelling correction taking into account surrounding context - library, (in russian) Исправляем опечатки с учётом контекста
- Embedding for spelling correction
- A simple spell checker built from word vectors
- What are some algorithms of spelling correction that are used by search engines?
- Moman - lucene/solr/elasticsearch spell correction/autocorrect is actually powered by this library.
- Query Segmentation and Spelling Correction
- How search|hub.io changed the way of working for Site Search Consultants
- Boosting the power of Elasticsearch with synonyms
- Real Talk About Synonyms and Search
- Synonyms in Solr I — The good, the bad and the ugly
- Synonyms and Antonyms from WordNet
- Synonyms and Antonyms in Python
- Dive into WordNet with NLTK
- Creating Better Searches Through Automatic Synonym Detection
- Multiword synonyms in search using Querqy
- How to Build a Smart Synonyms Model
- Giovanni Fernandez-Kincade. Bootstrapping Autosuggest, Building an Autosuggest Corpus, Part 1, Building an Autosuggest Corpus, Part 2, Autosuggest Retrieval Data Structures & Algorithms, Autosuggest Ranking
- 13 Design Patterns for Autocomplete Suggestions
- On two types of suggestions
- Improving Search Suggestions for eCommerce
- Autocomplete Search Best Practices to Increase Conversions
- Understanding BERT and Search Relevance
- Google is improving web search with BERT – can we use it for enterprise search too?
Awesome Spacy - Natural language upderstanding, content enrichment etc.
- Word2Vec For Phrases — Learning Embeddings For More Than One Word
- Gensim Word2Vec Tutorial
- How to incorporate phrases into Word2Vec – a text mining approach
- Word2Vec — a baby step in Deep Learning but a giant leap towards Natural Language Processing
- How to Develop Word Embeddings in Python with Gensim
- Automatically detect common phrases – multi-word expressions / word n-grams – from a stream of sentences.
- The Unreasonable Effectiveness of Collocations
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Knowledge graphs applied in the retail industry
Knowledge graphs are becoming increasingly popular in tech. We explore how they can be used in the retail industry to enrich data, widen search results and add value to a retail company.
- Daniel Tunkelang Query Understanding.
- Query Understanding, Divided into Three Parts
- Search for Things not for Strings
- Understanding the Search Query. Part 1, Part 2, Part 3
- Paper Unsupervised Query Segmentation Using only Query Logs
- Paper Towards Semantic Query Segmentation
- How is search different than other machine learning problems?
- Reinforcement learning assisted search ranking
- Baymard Institute 11 E-Commerce Search Articles
- Why is it so hard to sort by price?
- Faceted Sorting
- E-commerce Search Re-Ranking as a Reinforcement Learning Problem
- Locality Sensitive Hashing
- Minhash
- Better than Average: Sort by Best Rating
- How Not To Sort By Average Rating
- The influence of TF-IDF algorithms in eCommerce search
- One hot encoding
- Keyword Extraction using RAKE
- Anonymisation: managing data protection risk (code of practice)
- The Anonymisation Decision-Making Framework
- 98 personal data points that Facebook uses to target ads to you
- Opportunity Analysis for Search
- A/B Testing for Search is Different
- Discounted cumulative gain
- Demystifying nDCG and ERR
- Evaluating Search (by Daniel Tunkelang) Measure It, Measuring Searcher Behavior, Using Human Judgement
- When There’s No Conversion Rate
- Choosing your search relevance evaluation metric
- How to Implement a Normalized Discounted Cumulative Gain (NDCG) Ranking Quality Scorer in Quepid
- 5 Right Ways to Measure How Search Is Performing
- AI-powered search
- Relevant Search
- Deep Learning for search
- Interactions with search systems
- Embeddings in Natural Language Processing. Theory and Advances in Vector Representation of Meaning
- Search User Interfaces
- Search Patterns