My Search for Relevance on LinkedIn

by · March 21, 201414 comments

I’m a heavy LinkedIn user, and like many of my ilk — recruiters, marketers, job seekers — I’m quick to jump to search for people-finding. That’s because exploring a social network, connection-by-connection, is beyond tedious. All but poorly-connected users will have millions in their networks, within 2 or 3 links, and manual exploration simply doesn’t scale. (Cutting connections isn’t the answer; it destroys a network’s value, which grows steeply with the number of participants according to the network effect.) Further, you can’t explore what you can’t see. Because many individuals hide their connection lists, a connection-surfer won’t see entire branches of her network. So I — and you — use search.

Unfortunately, for all the professional social platform’s data-science mojo, elements of LinkedIn search are disappointingly weak. I’ll illustrate those elements and describe how LinkedIn can better help users discover business value in their connections.

The State of Search on LinkedIn

The search user interface gold standard is a simple text-entry box, and LinkedIn obliges, with the added possibility of using a drop-down to focus search on People, Jobs, Companies, Groups, Universities, Articles, and Inbox. (That latter label is strange, given that “Inbox” search searches all your messages. And by the way, I’m using the Web interface although my points apply even more to the mobile app, which, as is typical, offers reduced capabilities.) Or click on Advanced and get this window:

LinkedIn advanced search

And here we see the weakness. LinkedIn offers People Search, fine if you’re looking for individuals, identified by particular attributes, by particular facet values. LinkedIn does not offer search that exploits its essential identity; LinkedIn does not offer Network Search.

Your Networks, Via Your Connections

I’ll explain what I mean by “network search” by showing that the data is there, in LinkedIn. Consider a LinkedIn search results screen, mine:

LinkedIn Search Results

(Actually, this screen represents a null search; it’s the start of a listing of my connections.)

We have faceted navigation — the ability to filter on values of characteristics including distance of connection (1st, 2nd, Group) and physical location — and we can see network attributes, in particular, the number of connections I share with each individual.

I don’t have a clue how LinkedIn did the ranking here, despite the (fairly opaque) explanation the company provides. For instance, of the seven individuals you see displayed, I’ve met only two in person, and only one of them more than once, Howard Dresner. Howard and I share 138 connections; that our networks so significantly overlap is a strong indicator of profile similarity, his and mine. Yet the top-of-the-list individual and I share only eight connections, I’ve never met him in person, and I’ve interacted with his company on only one occasion. How did he get to be top ranked for me?

We’re in a damned-if-you-do, damned-if-you-don’t situation. If the answer to this apparently anomalous ranking is that it’s an unordered list, not a ranking, then the question is, Why not? And if the answer is that LinkedIn is applying a ranking algorithm that makes sense only to the company’s engineers but not to a user like me, then the questions are, WTF? and Why can’t I have the ranking my way?

I want to be able to order hits by descending number of shared connections. I can’t, and that’s because, again, LinkedIn offers only People Search when I also want Network Search.

It’s a Network, Not Just a Set of Nodes

LinkedIn hosts social networks. In social networks, the number of short connections between two individuals in the graph is a good indicator of similarity, of shared interests. But I don’t want only to rank search hits by number of connections. I want to use other interesting network properties in my searches. LinkedIn gets the concepts; they just don’t let me use them. What concepts? Clusters, or subnets if you will. Take a look at my LinkedIn InMap:

Seth Grimes's LinkedIn InMap, 2014 February 23

Each colored node-set represents individuals who share a certain affinity with me, based on network structure. LinkedIn says, “We use information about how people in your network are connected to you and each other to create your personalized map. Groups like colleagues, people you went at school with, or friends are separated into color-coded clusters, as people within these groups are also interconnected with each other.”

Network Search would allow me to limit searches to members of particular clusters and their connections. In my case, that would mean I could restrict search to my BI & data warehousing connections (the azure cluster toward the top of my InMap) or my market research connections (pea green toward the lower right) or my European information retrieval and text analytics connections (reddish, lower left), in order to improve results relevance. What I’m asking is for LinkedIn to support a new, dynamic search facet.

Relevance is key, and leads me to my final topic, recommendations, which I’d characterize as a passive form of search.

When Will They Ever Learn?

I won’t accept a connection request from a recruiter or sales person whom I haven’t actually interacted with, nor from a student who doesn’t have any non-academic accomplishments that are professionally relevant to me, nor a request from a non-professional contact such as a community member. I follow this rule because I use LinkedIn almost exclusively for professional networking, and mixing in these (literal) outliers would make it much more difficult for me to correctly target my outreach efforts.

LinkedIn "People You May Know" recommendations

So who does LinkedIn recommend, as People You May Know? Check ‘em out, in the image at the right. Nothing personal, but they are not people I will be connecting to, and LinkedIn should have known that.

One of them works professionally in an area related to mine, although not closely. A second shares one connection with me and lives near my community, with no professional connection however, and a third is three links away from me with our only similarity being that she also lives near my community.

I do, occasionally take LinkedIn connection recommendations, but never for people like these, and I have ignored many connection requests from people similar to them. I have done many, many LinkedIn searches, on terms such as “sentiment analysis” and “customer experience,” but never on terms that would turn up these people.

If LinkedIn did a bit of behavior mining — my search, profile viewing, and connection requests initiated or accepted — the platform’s People You May Know recommendations would be far more accurate. Machine learning could do the job; not a small task given the number and diversity of LinkedIn users, but then you don’t have to mine every user’s actions in order to come up with predictive recommendation models that would surely out-perform what’s in place now.

My Search for Relevance

I have high expectations for LinkedIn. I know that the company has great data-mining and information-retrieval capabilities, but also I pay them quite a bit. (I’ve been a Premium user on and off; I downgrade, to save money, for stretches when I’m not going to be using the platform extensively.)

So consider this article a challenge from a faithful user. Help me do my job better. Factor network characteristics and user behaviors into searches, and help me in my search for relevance.

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About Seth Grimes

Seth Grimes

Seth Grimes helps companies find business value in enterprise data and online information. He consults via Alta Plana Corporation, works as an industry analyst, organizes the Sentiment Analysis Symposium and tweets at @sethgrimes.

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Comments on Social Media Explorer are open to anyone. However, I will remove any comment that is disrespectful and not in the spirit of intelligent discourse. You are welcome to leave links to content relevant to the conversation, but I reserve the right to remove it if I don't see the relevancy. Be nice, have fun. Fair?

  • Alison Stoodley

    Excellent points! Let’s hope they are listening.

  • webmetricsguru

    Wonderful article Seth – and I might add, as someone who has looked at LinkedIn pretty closely over the last year or so as a research tool, spot on. They have a lot of good data, but lets face it, LinkedIn is the Matrix, they want your data so they can refashion it into a social selling index and sell it to recruiters and employers – in return they give users a free set of tools – but THEY AREN’t OPTIMAL.

    And why should they be …their real business is selling your Data – not serving their users.

    And while we’re at Linkedin bashing – how come the last interesting visualization tool they came up with – InMaps, is over 3 years old? You’d think by now they’d have their Oracle, Hadoop geared Data Science team come up with something more beyond a map you can only share as a graphic.

    I’m not knocking the map, some of my students have actually found opportunities from it – but why, with all this great data that LinkedIn sits on, can’t they come up with something better than an InMap that reveals, as you say, little of the really important relationships in the data that we know they have?

    Might it go back to the reality that LinkedIn runs their business to use your data, not democratize it? Ha! Musings on a Friday.

  • Marc

    To what extent can LinkedIn improve without spying more on people, for instance by agregating information from various social networks ? It this really desirable ?

    • http://sethgrimes.com Seth Grimes

      Marc, LinkedIn already has an alliance with SlideShare and some kind of Gizmo to hook in your Twitter feed and I don’t know what else. Cross-network sharing is a reality.

  • http://www.jojari.com/ Jojari Jobs

    I gather they use pre-ranking of their indexes so they can early terminate searches for speed. Those indexes appear context aware (e.g. “architect” would split on “software” / “construction” context branches), so i would guess you get the top ranked results matching your contextual bucket. If you request facets as well, it will interrogate the indexes only until enough results match the facets. So in short, i think the indexes are context bucketed & ranked, e.g. not personal, or they would need to re-rank them all for each user independently.

    As a side note, they probably could identify the clusters you mention and turn them into facets quite easily as this is already part of their query processing, but if they were to do as you wish, salesman and recruiters might not be so happy about it. :)

    Nice article though, insightful perspective on a tough problem.

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