I might be missing something obvious, but is there any way to get an insight into why the GSA results for a query are what they are? E.g. Lucene searchers have explain method. Is there anything similar in GSA?
This would be extremely useful when you don't quite understand why you are getting results that you are getting and why the order is what it is.
No. According to all expert reports in enterprise search domain (i.e. Gartner, but not only), Google never explained how it ranks search results in GSA.
Related
Is it possible to implement "people who searched for this, also searched for" feature with elasticsearch? Just like what we often see in the search result at the bottom of Google search. If possible, how can we achieve it?
There is this elasticsearch+Mahout based plugin available for implementing your solution. It may help you
https://github.com/codelibs/elasticsearch-taste.
Let's say I search for "ABLS" and the SOLR returns a result that to me does not make any sense.
How can I debug why SOLR picked this record to be returned?
debugQuery=true would help you get the detailed score calculation and the explanation for each scores.
An over view of the scoring is available at link
For detailed explaination of the debug information you can refer Link
You could add debugQuery=true&indent=true to the url and examine the results. You could also use the analysis tool in solr. Go to the admin and click analysis. You would need to read the wiki to understand either of these more in depth.
queryDebug will give you knowledge about why your scoring looks like it does (end how every field is relevant).
I will get some results that you are not understand and play with them with Solr's analysis
You should find it under:
/admin/analysis.jsp?highlight=on
Alternatively turn on highlighting over your results to see what is actually matching in your results
Solr queries are full of short parameters, hard to read and modify, especially when the parameters are too many.
And after it is even harder to debug and understand why a document is more or less relevant than another. The debug explain output usually is a three too big to fit in one page.
I found this Google Chrome extension useful to see Solr Query explain and debug in a clear manner.
For those who still use very old version of solr 3.X, "debugQuery=true" will not put the debug information. you should specify "debugQuery=on".
There are two ways of doing that. First is the query level, which means adding the debugQuery=on to your query. That will include a few things:
parsed query
debug timing information
detailed scoring information which helps you with analysis of why a give document is given a score.
In addition to that, you can use the [explain] transformer and add it to your fl parameter. For example ...&fl=*,[explain], which will result in your documents having the scoring information as another field.
The scoring information can be quite extensive and will include calculations done by the similarity algorithm. If you would like to learn more about the similarities and the scoring algorithm in Solr, have a look at this my and my colleague Radu from Sematext talk from the Activate conference: https://www.youtube.com/watch?v=kKocQdYGVJM
When searching for something in Google, if you misspell a word (may be by mistake or may be when you really mean this non-dictionary word), Google says:
"Showing results for ..... Search instead for .......".
I am trying to figure out how this would work.
This basically means being able to find the closest dictionary word to the non-dictionary word entered. How does it work? One way I can guess is :
count no. of instances of each character and then scan dictionary to find a word with same no. of instances of each character (only with +-1 difference). But this will also return anagrams.
Is some kind of probabilistic model of any use here such as Markov etc. I don't understand Markov well enough to throw it around but just a very wild guess.
Any insights?
You're forgetting that google has a lot more information available to it then you do. They track when people type in a word, don't select a result, and then do another search shortly afterwards. They then use this information to suggest better searches for you.
See How does the Google "Did you mean?" Algorithm work? for a fuller explanation.
Note that this approach makes sense when you consider that Google aren't actually doing spell-checking. Instead, they are trying to work out what search term will give you the answer you are looking for. Obviously there is a lot of overlap between this and spell-checking, but it means they are not always trying to correct a search for, e.g., "Flickr".
When you search something which is related to other searches performed earlied closed to yours and got more results, google shows suggest on them.
We are sure that it is not spell checking but it shows what other people queried the related keywords.
I have been testing a CSEs accuracy in comparison to google and it seems to fall down when I type in full urls with long query strings. Shorter keyword based and nice url pages are coming through fine.
At first I just thought the pages were not indexed, but they are on google.com and google.co.uk, the only problem is with my CSE. Hence the confusion.
Does anyone know if there is a fundamental difference between:
The ranking algorithm used
The datasets being used
The datacenters being used.
Anything else.
I have tried only allowing the specific site, as well as allowing results from the entire web.
To put is basically, can I reliably expect a CSE and Google's results to match or be very similar, assuming no variables?
No, the mismatch between google.com results and CSE results is a known issue. Google has said that they value speed of results over completeness, and that's just how it is.
This answer has been the same since 2007:
http://www.google.com/support/customsearch/bin/answer.py?hl=en&answer=141877
I've noticed that in search results by CSE are missing the ones from forums.
For example, how does StackOverflow decide other questions are similar?
When I typed in the question above and then tabbed to this memo control I saw a list of existing questions which might be the same as the one I am asking.
What technique is used to find similar questions?
I got an email from team#stackoverflow.com on Mar 20 that mentions how it works:
the "ask a question" search is
exclusively on title and will not
match anything in the body. It is a
mystery to me why people think it's
better.
The last sentence refers to the search bar, which I've found is less useful when I'm trying to find a specific question I've already seen.
I think it's plain old word matching. However, I might add that this feature does not work as well as I would like it to. It's much better to do google search with site:stackoverflow.com prefix than to rely on SO to provide the relevant suggestions.
Poorly -- using MS SQL Full Text Search, I believe. You'll have better luck using Lucene, IMO. For more background on the topic see the Wikipedia article on Lucene or the general topic of information retrieval.
The matching program would store an index of all questions. When you ask a question, all keywords in your question are matched against the index. This is similar to Google Search. Lucene open source search can be (and with high probability has been) used for this. Since the results are not quite accurate, I presume they index just the headlines of the questions, as an approximation.
The other related keyword is collaborative filtering, the algorithm popularized by Amazon to recommend products based on behavior of other similar customers. In the current case, an alternative algorithm based on collaborative filtering is: keywords are extracted from the question, then tags associated (in the history) with the keywords are found. Questions which have those tags are returned. Well, experiments are needed to see whether it works well at all.