In technical terms, job boards are medieval.
The purpose of a job board is to connect employeers and candidates, in an efficient manner. Matching employees and candidates is tricky, and the boards that I have seen (and used) do very little to match them.
Boards have two collections: employers and job seekers. Employers must register and create a description of the offered job, and employees can then come and search through the list. Some boards are more sophisticated than others; simple boards list the positions, mainstream boards allow users to search on various criteria (location, skill keyword, date posted), and really savvy boards let job seekers set up agents that perform searches and report results.
The search mechanism is what I find disappointing. It is a keyword-based match, and nothing more. Some job boards allow for multiple keywords and you can specify that all keywords must match. Some job boards fail to recognize certain keywords, such as "C#" or "C++". But the searching is still pretty brute-force.
The problem is that searches yield too many results. A search in a metropolitan area can yield hundreds of matches. One doesn't want to specify too many keywords in fear of locking out a good position that omits a keyword from the description. But too many results is not effective -- after about the twentieth match (of which most are not so great) one's brain becomes fuzzy. The quantity of search results is high. The quality of search results is not so high.
The problem happens on both sides, that is for employers and job seekers. Either can be overwhelmed with responses.
Web 2.0 techniques can help.
Instead of brute-force keyword matches, job boards can let users rate the search responses and use the ratings in future searches. This is the technique used by Netflix to recommend movies.
Instead of movies, job seekers can rate offered jobs. At first (that is, right after registration) the job board knows only the technical skills specified by the user. As the job seeker looks at offered jobs, the user can rate the match and the job board can gain additional knowledge of the job seeker's desired job.
The system works for the employer too, although the job board must keep a set of ratings for each job posted, not a single set. This allows an employer to create a new posting and start with no preset ratings -- which you would want if the previous job was for a COBOL programmer and the new job is for a C# programmer.
The system is not exactly the same as the Netflix algorithm which uses "what other people recommend" -- that method doesn't make sense for job boards. And the overall pool of candidates is changing: as people find jobs they take themselves out of the pool, whereas movies are returned in the Netflix model.
A smarter search should allow for faster matches of employer and job seeker. That is a good thing, if the job board is paid for making such connections. If a job board is paid by advertisers and not by employers or job seekers, then the dynamics change. In that situation, the job board has little incentive for people to find jobs. The incentive is to have people visit the site and see advertisements.
Job boards have been succssful by gaining bulk, that is, the number of openings and the number of job seekers. Some have gained business by using clever advertising. But bulk and advertising is not enough in this world -- you need results. Job boards that deliver lots of poor matches will be at risk of a competitor that delivers fewer high-quality matches.
It's time for job boards to move beyond the medieval approach of brute-force high-quantity results and into the age of intelligent results.
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