Each result is categorized by its respective domain.
4/ We use these ratings to filter and re-rank what results show in what bucket.
Categorizing domains is not a perfect science, so we make sure to show results at most one bucket away from the selection on the slider.
5/ With this bucketing implementation, we need to ensure that we have domains to serve from all of these perspectives.
We collected a variety of domains from each of the 5 buckets, and pulled the respective sitemaps.
These sitemaps are fed into our crawl pipeline.
6/ Previously, crawling and indexing one URL into Neeva’s own index took more than 2 weeks after the URL’s discovery.
Apparently, this is too stale in terms of serving news pages.
7/ In order to serve news pages in a fresh way, we build our fresh crawl-indexing pipeline.
Every hour, we crawl and index URLs from a couple of sources, including:
📌 Sitemaps
📌 Twitter feeds
📌 API crawl, etc.
From there we fast-track these pages into our Koala indexing. 🐨
8/ To utilize Bias Buster to its full potential, we implemented triggering logic.
This allows the slider to show if there are a variety of results to view on the spectrum.
We determined these queries are typically ones that have a high news intent, as well as political intent.
9/ So, we:
1️⃣ Probe the result sets pulled from the buckets to gain intuition on variety
2️⃣ Check political intent & topicality
3️⃣ Check if the query has any identified intents for which we shouldn't trigger on
10/ Here's an example...
If the query includes a site restrict, we wouldn't want to display Bias Buster, since the ultimate intent is to see results from that site.
11/ Overall, Bias Buster gives an opportunity for our US users to explore different perspectives on the political spectrum when available.
Head over to neeva.com to try it out and let us know what you think!
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1/ It's not the size, it's the skill - now releasing #Neeva's Query Embedding Model!
Our query embedding model beats @openai’s Curie which is orders of magnitude bigger and 100000x more expensive. 🤯
Keep reading to find out how... 📖
2/ Query understanding is the life blood of #searchengines. Large search engines spent millions of SWE hours building various signals like synonymy, spelling, term weighting, compounds, etc.
3/ We solve the problem of #query similarity: when 2 user queries looking for the same information on the web.
Why is this useful? Query-click data for web docs = strongest signal for search, QA, etc.; solving query equivalence => smear click signal over lots of user queries
1/ Google will do just about anything to maintain its monopoly power
Fear-inducing pop-ups with misleading designs to trick users into going back to Google search ✅
What’s a competing search engine to do? The only thing we can…Design and innovate our way out of it! 🧵
2/ By default, Google Chrome comes with Google search – no surprise there.
However, if a user prefers a more privacy focused search engine, they have to jump through a few hoops to install an extension and make it as the default.
All in all not terribly difficult so far, but…
3/ The last thing Google wants is to lose a user, especially from their cash cow – search.
So, under the guise of security, upon installing a new search extension such as Neeva and attempting your first search from the omnibar, they deploy the misleading warning prompt.
1/ When someone types “neeva” into search, how do we know they mean “neeva.com” instead of “neevaneevaneeva.com”? After all, the second has 3 times as much neeva!
See how you can do much better than vanilla TF-IDF / cosine similarity for textual relevance!🧵
2/ Textual relevance is only one part of document ranking (alongside signals like centrality, page quality, and click rate)
But it’s one the most important parts and the one we’ll be covering in today’s thread.
3/ The most popular way to rank documents relative to queries is to use TF-IDF vector representation.
Essentially, this claims the more often a term occurs on a page (TF), and the less often it occurs on other pages (IDF) the more likely that term is to be relevant to the page.