Pinterest explains how it’s making its search work better

It’s not just Netflix that’s taking search seriously through the use of recommendations. Pinterest is amping up its search capabilities to provide better results based on the words a user is searching for in relation to what other people may be searching for, the company detailed in a blog post on Monday.

Dong Wang, the Pinterest software engineer who wrote the post, explained that even though a user may search for the word “turkey,” it’s unclear what exactly that person may be looking for. Does he want to find turkey recipes, is he planning a trip to Turkey or is he just interested in poultry — it’s hard to say without some context.

If that person decides to search for “turkey recipes” as part of his next query, Pinterest takes that into account and can assume that the next person who may be searching for “turkey” might also be craving some turkey recipes as well; maybe it’s holiday season and everyone’s hungry. Pinterest learned that “the information extracted from previous query log has shown to be effective in understanding the user’s search intent” and this can be applied to other Pinterest users as well.

Pinterest uses a data-collection workflow called QueryJoin that helps with applying one user’s search queries and the data gleaned from those searches to other users in order to generate more relevant search results for everyone involved. QueryJoin contains data like search queries, demographic statistics, adjacent queries and pins.

Pinterest QueryJoin

Here’s some technical details on QueryJoin, per the blog post:
[blockquote person=”Pinterest” attribution=”Pinterest”]For each Pin, we have aggregated data from the PinJoin (the data collection of a cluster of Pins with the same image signature and the information about those Pins) as well as some engagement stats like the number of clicks, repins and likes.][/blockquote]

The data collected by QueryJoin is used in several Pinterest search functions such as autocomplete, guided search and search relevance.

ThoughtSpot’s data analytics hardware is now available to the public

Big data startup ThoughtSpot said Tuesday that its core product, the ThoughtSpot Relational Search Appliance, is now available to the general public. ThoughtSpot wants to bring a Google-like search experience for data analytics with its hardware appliance, which contains an in-memory database and a custom-built search engine. When a user types in keywords into the search interface, ThoughtSpot can predict what a user wants to find and run SQL queries based on the search. The startup — whose founding team includes former members of Nutanix, Google and Yahoo — landed $30 million in funding in June.

Elasticsearch raises $70M and is taking JSON analysis global

Elasticsearch, the company behind a very popular open source suite for indexing, searching and visualizing JSON documents, has raised a $70 million series C round of venture capital. Just more than two years since being founded, the company has raised $104 million.

Russia launches state-controlled Sputnik search engine

Russia’s new Kremlin-friendly search engine Sputnik – planned since last year — reportedly achieved lift-off on Thursday. As spotted by on Wednesday and confirmed to me today by local sources, Sputnik was launched on Thursday by state-controlled Rostelecom. Recent reports suggest the venture cost $42 million to develop and Sputnik, unavailable from outside the country, will be the default search engine for government departments and state-controlled companies. Russia is increasingly keen on censoring the internet there, and having an amenable search engine will prove useful to the authorities … if they can get significant numbers of people to switch from rivals such as Google(s goog) and market leader Yandex. Sputnik’s name may harken back to past days of technological glory, but it’s also fitting for these days of Cold War revivalism.