I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
word2vec isn’t just for words (daily search tip)
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In my previous tip I introduced word2vec. I discussed it in terms of language: this word, mary, shared context with this other word, lamb, so their embeddings move closer. Why constrain ourselves to language? We could pretend that “Doug likes Star Wars” is the same kind of co-occurence. We can make a table of users to the movies they like: Anchor Positive movie Negative movie
doug star wars king kong
doug star trek cinderella
tom star wars citizen kane
tom battlestar galactica the aviator
Think about what we have:
Thus now, we have a movie recommender system, through the same technology behind word2vec. We could use this for quite a lot of domains:
And so on! -Doug PS - 5 days left to signup for Cheat at Search with Agents! Events · Consulting · Training (use code search-tips) You're subscribed to Doug Turnbull's daily search tips where I share tips, blog articles, events, and more. You can always manage your profile: |
Doug Turnbull
I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Reviewing Bayesian BM25 - a new approach to creating calibrated BM25 probabilities for hybrid search. I talk about this vs naive approaches I've used to do similar things. Enjoy! https://softwaredoug.com/blog/2026/03/06/probabilistic-bm25-utopia -Doug Events · Consulting · Training (use code search-tips) You're subscribed to Doug Turnbull's daily search tips where I share tips, blog articles, events, and more. You can always manage your profile:
You may know BM25 lets you tune two parameters: k1: how quickly to saturate document term frequency’s contribution b: how much to bias towards below average length docs What you may NOT know is there is another parameter k3 What does k3 do? It handles repeated query terms. Old papers suggest k3=100 to 1000, which immediately saturates. That’s why Lucene ignores k3. It just uses the query term frequency. Some other search engines like Terrier set it to 8. So for the query, “Best dog toys for...
Rare terms have high inverse document frequency (IDF). BM25 scoring treats high IDF terms as more relevant. Why? We assume if a term occurs rarely in the corpus, it must unambiguously point to what the user wants. It’s specific. But that’s not always true. Not all text is created equal. Corpuses violate this assumption frequently. Why? No need to use a common term - Book titles may rarely mention the word “book”, but clearly “book” in a book index has low specificity. Language gaps between...