Nerdy Custom Search on GitHub

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Ever since we started making use of structured web search capabilities, I have been fascinated by the precise results we can obtain from a general search engine via this mechanism.

Here is an investigation and multiple examples of precise X-Raying on, which is a truly useful site to know if you are looking for software developers.

For those (few) Sourcers who love working with advanced search operators – here is where you will find tons of long and barely readable expressions, that you can change to your liking! For your convenience, I have placed a search box for Custom X-Raying Github below as well.

For those who’d rather skip the cryptic search syntax, the good news is that we have a new, unique Sourcing Tool in development – currently, in a stealth mode – that will relieve end users of needing to use these operators. (Please follow us for more news!)

For now, just take a look at the search results. You will find them to be precisely finding what we are looking for in each case – a big difference with “general” Google and most social sites.

Here you go:

Examples – Github Precise XRay Member Search

Want to build your own? The Custom Search Engines Webinar is coming up!

And here is a

Github Custom XRay Engine

This Webinar will Change the Way You Source

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“Like writing poetry or tweeting, searching without operators allows us to be creative and make discoveries.” – David Galley

Searching without Boolean is a new webinar with how-to, step-by-step guide to creating amazing searches in English; here is a quick write-up on its content.

If you search the Web on a regular basis, you will want to obtain the webinar materials, to instantly improve your sourcing performance. Promise, you will never search like you used to! Tune into the best ways to build queries, that are now simpler and cleaner than before, yet produce superior results.

Recruiters: want to get on the phone with potential candidates fast? The webinar teaches more than a dozen easy to use “plain English” search patterns, which do not require advanced search skills. (But they do require thinking it through and understanding what to expect; we’ll go over that in the presentation).

Major search engines, including Google, are rapidly becoming more semantic-oriented, meaning – they try to guess the intent of the person who Googles and respond appropriately. In many practical cases, Google is now giving better answers to searching “in English”, without advanced operators, than when using complex Boolean constructions. That’s great news!

At the lecture, I demonstrate finding “hidden” professionals, locating and exploring previously unknown sites full of candidate details, by using creative patterns and templates, searching in English “without Boolean”.

Equipped with the search patterns that are easy to keep in mind and apply, you will be able to put your new skills to work as soon as the webinar ends.

Who should attend: Recruiters who need to find target professionals outside of the mainstream sites, but dislike or don’t have enough time to write complex Boolean Strings. Everyone who wants to find more professional information on the web by using additional search patterns.

Included: The slides and video recording from the session(s) for you to keep and one month of support for any questions about the Boolean free Sourcing techniques demonstrated in the webinar.

The webinar materials are available (including one month support): Searching without Boolean.

Search Like a Pro in Three Simple Steps

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Want to search the Web like a Pro? Here is a fast way to get there.

Searching the Web is not Rocket Science. Everyone can learn to search even for complex information, such as professional bios, quite efficiently, following the three steps that I am about to describe. And the good news is, you do not necessarily have to use advanced search operators to find excellent results.

Steps 1 and 2 are required to prepare to search, while Step 3 is actual searching.

Step 1. Identify where you want to search.

The web is so large – just Googling some keywords, that you would like to see on target profiles, is a long shot! The first step is to narrow down the set of pages where you would like to find the results. A traditional technique for this step is only to look within a given site. Searching only within a given site, using a search engine, is called “X-Raying.”

(Whether you are or are not familiar with the operator site:, just read on. You won’t necessarily need to learn about search operators.)

To find a site that you might want to X-Ray, you can start with a profile you have already found – perhaps linked from another profile or shared by a colleague. Or, if you are starting from scratch, run a simple Google query describing a site. For example, Google for [association of accountants in Canada] or [members oil gas Louisiana].

Step 2. Create a search template to search just for the desired pages on the site.

For that, it helps to take a look at a few profiles on a site (that’s if those are your desired pages) and see what these pages look like; then, use that to start constructing a search. Let’s take a look at an example.

Here is a sample profile:

For people who are familiar with the “formal” X-Raying (i.e. using the operator site:) – from looking at the profile, you will see how to proceed to narrow the search down to this type of pages – at least in this example. You can take advantage of the URL structure:

For those who don’t want to use any operators: use the domain and add a phrase or keywords that any profile on the site would have. Example:

“” “member profile.”

Either of the two sample strings above can serve as a search template.

Depending on the site you are looking to explore, you may find that the site’s profiles have a phrase like “member since”, “connect with”, or “full profile it’s free” (etc.) – those would be the phrases to use in the template.

Step 3. Now you are ready to search.

Add keywords, such as job titles, locations, and skills.

Examples (using the two templates we had created earlier):

You can repeat the search varying the keywords and phrases, to uncover the pages that you want to find. Here’s a diagram with several more examples of the three-step search construction (no Boolean operators are involved!):


For an in-depth exploration of fast and productive searching without special search operators, please join me at the upcoming Webinar

Searching without Boolean

(Lecture – Wednesday, September 7th, 2016. Practice – Thursday, September 8th, 2016. Seating is limited.)

If you do know how to use advanced search operators, I invite you to join as well – to review your search templates, learn how to simplify your searches, and get the results fast.



Who Offers Proximity Search?

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Finding several terms that are close to one another is a way to make the search results more relevant, i.e. make the search more semantic. This feature is called Proximity Search; it’s especially useful when searching on the web and in long, unstructured documents. A familiar example is to search for the word manage close to the word people, to find bios of those who have managed people, vs. profiles that just have both words somewhere in the text. Another example would be to look for a school name close to the year of graduation. Applications of proximity search are multiple.

Google, Bing, and Yandex have all implemented proximity search features. It looks like, however, the first two search engines have dropped the ball. Let’s take a look at some examples.

Google’s has never officially documented its proximity operator. Back in 2010 researchers were excited to read the post “AROUND has always been around” by Google’s Dan Russel. At this time, however, after looking at multiple test searches, I’d say AROUND doesn’t do its job. Compare, for example, these (randomly picked) searches on Google:

  • manage AROUND(9) people “associate partner” accenture atlanta
  • manage AROUND(1) people “associate partner” accenture atlanta
  • manage people “associate partner” accenture atlanta

The results are just identical:


Perhaps there are some cases where AROUND does influence the results, but the operator is certainly unreliable!

The same goes for Bing’s (documented!) operator NEAR. Just compare these simple searches and you will see that it’s not working right:


Let’s hope that Google and Bing engineers will make the proximity search feature a high enough priority to fix it sometime in the future. is the only global search engine that does, in fact, currently support proximity search. For starters, Yandex has the operator & which means “search for the terms to appear in one sentence.” Here is an example I have just created in response to a question on the Boolean Ning Network:

“graduated” & “Georgia Institute of Technology” & 2016

Further, Yandex can search for one term after another, within a certain “distance” in words between the terms. This search –

“Georgia Institute of Technology” /2 2016

will look for the second term (2016) to appear after the first term (“Georgia Institute of Technology”) with no more than two words in-between. You can also search using (for example) /(-2 +2)  to find the terms in either order, within two words from each other.

Conclusion: Yandex wins! If you are looking to use proximity search on the web, Yandex is your search engine.

Your comments and creative proximity search examples are welcome!


Have You Retrained Your Team on LinkedIn Recruiter?

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The new LinkedIn Recruiter (LIR) has some interesting and useful features. Unfortunately, it also offers unique search syntax that is hard to remember or understand.

Here’s an example. Guess which job title search will provide more results:

  1. Software Engineer
  2. Software Engineer NOT Senior

If you said “1.”, you are wrong – it’s the 2nd search that will return more results, and here’s why. When we type (just) Software Engineer, LIR automatically assumes that we are searching for a phrase Software Engineer.” But if we use AND, OR, or NOT, it doesn’t assume so. Therefore, the first search will not return (for example) Software Development Engineers, while the second search – would. The search syntax is not intuitive and not consistent with the syntax used in personal and business accounts – and is also not consistent with what it used to be in Recruiter.

The above is probably the most confusing part of the new design; I have described it in a recent post – Beware of the “Smart” LinkedIn Recruiter Search Syntax Change.

Interestingly, the new LIR has kept the “true” Boolean in the keywords search (and also in the only available documentation). If you want to stay with the “normal” Boolean syntax in LIR, there is another opportunity to do that: within “Filters” in Projects, the syntax remained as it used to be. But using a project’s filters is not a very powerful way to search because the profile previews are short.

By now, unsurprisingly, we have heard from multiple recruiters that the search results are not what they expect them to be in LIR.

Additionally, the feature I liked, the toggle between a search dialog and the Search Insights is gone. The “Search Insights” is still there (though it’s now hard to find), but there’s no easy way to switch between the two views.

The new LIR has also lost the valuable display of the distribution of the search results along the facets, such as the current or past company, location, and more. I.e., if you enter some qualifications and want to know which companies employ your target potential candidates, this is no longer possible in LIR.

There’s a lot more your team needs to be aware of if you are LIR subscribers. Join me  – and many of your colleagues – tomorrow at the webinar “Mastering LinkedIn Recruiter” where we’ll go over all the necessary skills, knowledge, and productivity tips (many of which are not part of the help documentation) that you need to navigate the Recruiter.

(If you miss the webinar – no worries – the recording, slides, and support will be available on our SourcingCertification site).

10 Little-Known Strings to Mine Member and Attendee Lists

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Many Sourcers know how to locate lists of professionals in Excel format by using the operator filetype: and the typical column headings, such as name, title, company, and email. Indeed, by adding keywords to a search like filetype:xls name title company email we can find lists of professionals whose background we can further investigate to see if they match our requirements.

There, however, lots of other ways to search for lists, that we can come up with, based on creative thinking and common sense combined (which is always a good recipe for successful sourcing!).

It will be up to the reader to figure out why these strings work and how to adjust them to work for your cases – but it shouldn’t be hard. Here you go:

Sorted Lists

Updated Lists

Email Domains

Personal Names

Job Titles and Abbreviations

The possibilities are endless!

I will be sharing numerous ways to mine lists and professional profiles both from the Surface and Deep Web in the special repeat of our most-attended lecture ever – “Sourcing without LinkedIn” on July 27th – don’t miss it!

LinkedIn Network Trimming

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LinkedIn is automatically removing many connections between its members today, July 18th, 2016, following an email it sent to all “mega-connectors” (including me). A copy of the email LinkedIn has sent to mega-connectors about the Trimming Project can be found, for example, in these blog posts: Further LinkedIn Changes Agree Or Disagree ? and Why Is LinkedIn Blowing Up My Network?

Some members think that cutting connections is “Microsoft influence”, but I don’t think so; it will probably take a while for Microsoft and LinkedIn to finalize the deal. Reducing connections was not surprising, as the next logical move after recently stopping the largest networkers from growing networks further.

Before I proceed to write about today’s network reductions in more detail, let me say that I am not annoyed that LinkedIn is doing that. Consider these two points:

1. Our real-life connections with fellow professionals do not equal our LinkedIn connections, and, of course, we can keep them even if the computer-recorded ones are gone!

In the end, connections on LinkedIn amount to computer-stored information. “Connecting” and “messaging” functionality has provided ways to form and expand business relationships (thanks to LinkedIn!). It’s a tool that helps, but then it’s up to us to work and communicate with others in real life to each others’ benefit.

2. LinkedIn is a business making significant income from InMails, i.e. messages between the members who are not connected. It’s only natural that LinkedIn won’t want  “too many” connections between members.

(Additionally, due to recent functionality changes, larger networks on LinkedIn have lost their advantages. For example, now only a small percentage of mega-connectors’ connections – and “followers” alike – are notified of the posted blogs and status updates. Instead, posts that make it to everyone’s streams are the ones most commented on.)

LinkedIn has created and continues to grow the world’s largest social network for professionals. I see some current functionality problems and some desired functionality added as more important that cutting down connections. (I’ll write another blog about those.)

With all that said, let’s get to some details on the LinkedIn Network Trimming project.

There’s no way to estimate precisely how many mega-connectors (members with 30K+ connections) there was before today, but the number was probably at least in the thousands.

Connections that LinkedIn is automatically removing today are the ones mega-connectors have made more recently. This is unfortunate because the most recent connections have often happened around some latest business discussions.

With a connection is removed, here is what to expect:connection-removed

I.e., if you are a mega-connector – or recently connected to someone who is a mega-connector – you are losing all of each other’s contact information (phone numbers, physical addresses, Skype IDs, etc.).  The notes and contact info of the 1st-level connections, except for email addresses, is information that was never possible to export, so it is just gone. You also won’t be able to send messages to each other any longer; it will have to be InMails.

As an additional side effect of removing “extra” connections, expect everyone’s visible networks to become smaller. LinkedIn search results will show more anonymous results (those “outside of your network”) than before.

It’s a good moment for mega-connectors, “LIONs” to rethink how they want to proceed!

As a temporary “stay in touch” mechanism that I have shared with some of my connections, please “connect with me” here on the blog. Also please feel free to join our Boolean Strings Facebook Group.


P.S. Interestingly, someone has forwarded a LinkedIn Rep reply when he enquired about removing connections. Here it is:support


20 Strings to Track CXO Appointments and Resignations

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Clearly, we all benefit from staying on top of industry news, and that includes appointments of new C-level executives. Here are twenty (20) Boolean search strings to simply look those up on Google’s web and news search. I have used “cloud computing” as an additional key phrase for these examples.

  1. “cloud computing” “chief * officer” “will be replaced by *” (news)
  2. “cloud computing” appoints “chief * officer” (web)
  3. “cloud computing” appoints “chief * officer” (news)
  4. “cloud computing” “chief * officer” “steps down” (web)
  5. “cloud computing” “chief * officer” “steps down” (news)
  6. “cloud computing” “chief * officer” “retired after” (news)
  7. “cloud computing” “new chief * officer” (web)
  8. “cloud computing” “new chief * officer” (news)
  9. “cloud computing” names “as chief * officer” (web)
  10. “cloud computing” names “as chief * officer” (news)
  11. “cloud computing” “new role” “chief * officer” (news)
  12. “cloud computing” “become chief * officer” (news)
  13. “cloud computing” “joins * * as” “chief * officer” (news)
  14. “cloud computing” “new COO” (news)
  15. “cloud computing” names “new CIO” (web)
  16. “cloud computing” names “new CIO” (news)
  17. “cloud computing” “resignation of” (news)
  18. “cloud computing” “has resigned” (news)
  19. “cloud computing” “promoted to” chief (web)
  20. “cloud computing” announces “new chief” (web)

If we’d like to look up CXO appointments that have happened in the past, we can reuse each of the above templates along with a restriction on the date range. Example:

“cloud computing” appoints “chief * officer” (shows results up until the end of 2014).

If you are interested in Recruitment Research, check out Martin Lee’s comprehensive, highly praised presentation, coming up (for the second and the last time) on July 20th, 2016 – Recruitment Research – What, When, and How.

Wrong Search Results? Personalization vs. Relevance

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When we search for information – such as resumes or professional bios – on a site, the following factors affect our productivity:

  1. The site does have the desired info (say, it has some profiles that match a job description)
  2. The site’s search system allows to easily find the info
  3. When there are multiple search results, those shown first are the most relevant, i.e. satisfy our goals in search

In this post, I’d like to discuss search results relevancy (p.3 above) – on LinkedIn and outside of it. On LinkedIn, there is – and always has been, as the result of its design – a challenge for the system to “decide” which results to show first.

LinkedIn is often said to be a modern version of a job board, but there are, of course, multiple differences. What recruiters often overlook when searching for potential candidates on LinkedIn, is that the “top” search results may not match what they want to find. This is because recruiters play a dual role. First, they are the social network search system users who want to find profiles with the desired skills and experience, regardless of who is searching (like on a job board). Second, they are the social network members with filled out profile information and a given set of the 1st level connections. The latter role often negatively affects the search.

When a LinkedIn member searches for people, he or she may do so for the purpose of connecting; in this case, the similarity of the industries and skills, a close-by location, and existence of connections in common play an important role. However, when a recruiter is looking for potential candidates using advanced people search, their personal network ideally should not affect the results a whole lot.

We don’t know how relevance is defined; LinkedIn, over the years, continues experimenting and dramatically changing what to show as the “top” results, without providing much consistency in its approach. But we’ve seen, from experience, that LinkedIn always shows search results that are affected by the searching member’s profile and personal network. (LinkedIn Recruiter searches are affected by personal networks as well). Personalization creates a disadvantage for recruiters.

A long time ago now, we had several options for the order of search results; that helped to bring the best results to the top of the list. However, the different sorting functionality has long been gone from LinkedIn.

In search systems outside of the business social network (and other social networks such as Facebook), we have easier control of the search results relevancy. From searching in resume databases to X-Raying membership sites, we can expect the results of a well constructed search to match for requirements. Additionally, many search systems offer various sorting options – that also helps. (Indeed, Zoominfo, Github, and Custom Search Engines are just a few examples of sites that offer different sort order of the results).

Let’s explore the vast search world outside of LinkedIn. Please join me for the upcoming – and our most popular – lecture Sourcing Without LinkedIn – coming up soon, on Wednesday July 13th. As always, those who sign up will get one month of unlimited support. Don’t miss the webinar!





Beware of the Smart LinkedIn Recruiter Search Syntax Change

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If you use LinkedIn Recruiter (LIR) and have upgraded to the long-awaited new User Interface and Search – have you noticed that previously working searches no longer produce the expected results? The idea behind the redesign was to provide suggestions – for example, for job titles. It’s a good idea and should be of help, but…

Let me outline what has happened around the switch to the new UI.

LinkedIn has changed the search syntax – in LinkedIn Recruiter only. To illustrate how, let’s take a look at a search from a personal account (any, premium or not):

linkedin search - 1


The keywords are combined by default. The AND operator is assumed by default, as we expect it to be, as it works by now in most search systems, including Google, resume databases, and people aggregators. This is how search has worked in LinkedIn and LIR until recently. But it no longer works like that.

Here is what it looks like in LIR, when I enter the same search parameters (the window on the left):


Why did the same search get fewer results? The reason is, LIR doesn’t assume AND any longer. Instead, it surrounds your keywords with the quotation marks by default. (That is, unless you insert a Boolean search operator – AND, OR, or NOT). LIR runs a different search than it did before the redesign. Compare the search in the left and right windows, and you’ll see.

To get the results for (just) the keywords combined, now you would need to enter AND explicitly:


Bottom line:

AND is no longer optional in LIR; the quotation marks are added by default around keywords – UNLESS you use any Boolean operators (AND, OR, or NOT).

Are you aware of the change? Do you find this confusing? Please comment below.

Here are some of the consequences:

Your saved searches may be broken. Your search history, too, will have entries that may or may not work as before. The link “View the results in recruiter” when you search from a personal account will take you to the same results as in personal; but if you start editing a field and exit without changing, the results will be off.