Search is alive and well. I'd recommend reading some of the latest textbooks and research papers on information retrieval. The industry was given new life about 5 years back with knowledge graphs and has been reborn again with recent innovations in machine learning, cloud computing, and data mining technologies.
I'm working on a project now which has indexed billions of pages and answers queries similar to a web search engine like Google:
https://www.AtSign.co/
The only difference is that it's a keyword + location based business contact information engine but operates on the same principles as a real web search engine client.
We're a small team and it would have been unthinkable even a years back to launch something of this scale effectively ... But here we are! Amazing space to be in right now
ugh. compare the results for tech support bridgeport, ct to google. or just look at them without comparing to google. awful! No offense but you aren't even doing the most obvious rule based filtering/ordering on cities/states in your result sets.
hi Greg, we don't offer filtering by cities at the moment, only states/countries, so, you wouldn't have been able to look up, "Bridgeport" specifically, right? A lot of people punch in a city and hit "search" but what they get as a response are matches from "any country" which is the default. Which is why you didn't see the basic filtering you were expecting.
Regardless, I just looked at our results for tech support in CT and I agree we need to work harder on our results, but comparing to Google, they only had tech support jobs (not even business listings)... which makes sense in their product use case.
I can see what you're saying and Google is by far, the industry benchmark, but it's also difficult to compare results sometimes ... it's like Apples and Oranges.
I don't want to get too much into the weeds, but there is a whole subset in Information Retrieval which relates to IR system evaluation, or search engine result evaluation. One simple way of doing it is simply labeling the accuracy of each result via human curator as either a 0 or a 1.
But it can get really complicated, for example, sometimes there just aren't relevant documents in the index in the first place ... so, you can't really blame your ranking factors too much. The opposite can happen too, where a word occurs too frequently in which case you might resort to other kinds of ranking factors (most notably pagerank).
In our case, we're focused on broadening our state/country level coverage right now for keywords (more listings), then we're going to focus on making sure our location accuracy is a lot better (it needs work). Overtime, you should get the results you're expecting more often :)
I'm working on a project now which has indexed billions of pages and answers queries similar to a web search engine like Google: https://www.AtSign.co/
The only difference is that it's a keyword + location based business contact information engine but operates on the same principles as a real web search engine client.
We're a small team and it would have been unthinkable even a years back to launch something of this scale effectively ... But here we are! Amazing space to be in right now