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.
Related
As asked in the title I'd like to know which ER-model notation Elasticsearch defaults to.
In terms of context, I've received a database schema which I cannot openly share, but the following screenshot shows one of the obscure looking relations.
After a few quick Google searches I was surprised at the fact that I couldn't find any official statement about Elasticsearch's default notation. While searching I came across this post where the OP showed a similar screenshot, but without the obscure double-dashed-line notation: https://discuss.elastic.co/t/convert-relational-schema-to-elasticsearch-mapping/72291
Is this Crow's Foot Notation, or something else?
there's no official model notation that is used for Elasticsearch, so it'd come down to whatever the tool that built this uses
I have users that have authenticated with a social media site. Now based on their last X (let's say 200) posts, I want to map how much that content matches up with a finite list of keywords.
What would be the best way to do this to capture associated words/concepts (maybe that's too difficult) or just get a score of how much, say, my tweet history maps to 'Walrus' or 'banana'?
Would a naive Bayes work here to separate into 'matches' and 'no match'?
In Python I would say NLTK can easily do it. In Ruby maybe gem called lda-ruby will help you. Whole LDA concept is well explained here - look at Sarah Palin's email for example. There's even the example of an app (not entirely in Ruby, but still) which did that -> github.com/echen/sarah-palin-lda
Or maybe I just say stupid things and that can't help you at all. I'm not an expert ;)
A simple bayes would work in this case, it is highly used to detect if emails are spam or not so for a simple keyword matching it should work pretty well.
For this problem you could also apply a recommendation system where you look for the top recommended keyword for a user (or for a post).
There are a ton of ways for doing this. I would recommend you to read Programming Collective Intelligence. It is explained using python but since you know ruby there should be not problem to understand the code.
I have tags on my website, and I input them one by one when I create a blog post. I love gmail's new feature, that ask you if you want to include X in a mail, if you type Y's name and that you often include both of them in the same messages.
I'd like to do something similar on my website, but I don't know how to represent the tags "related-ness" in an object or database ... thoughts ?
It all boils down to create associations between certain characteristics of your posts and certain tags, and then - when you press the "publish" button - to analyse the new post and propose all tags matched with your post characteristics.
This can be done in several ways from a "totally hard-coded" association to some sort of "learning AI"... and everything in-between.
Hard-coded solutions
This are the simplest algorithms to implement. You should first decide what characteristics of your post are relevant for tagging (e.g.: it's length if you tag them "short" or "long", the presence of photos or videos if you tag them "multimedia-content", etc...). The most obvious is however to focus on which words are used in posts. For example you could build a mapping like this:
tag_hint_words = {'code-development' : ['programming',
'language', 'python', 'function',
'object', 'method'],
'family' : ['Theresa', 'kids',
'uncle Ben', 'holidays']}
Then you would check your post for the presence of the words in the list (the code between [ and ] ) and propose the tag (the word before :) as a possible candidate.
A common approach is to give "scores", or in other word to put a number that indicates the probability a given tag is the right one. For example: if your post would contain the sentence...
After months of programming, we finally left for the summer holidays at uncle Ben's cottage. Theresa and the kids were ecstatic!
...despite the presence of the word "programming" the program should indicate family as the most likely tag to use, as there are many more words hinting.
Learning AI's
One of the obvious limitations of the above method is that - say one day you pick up java beside python - you would probably need to change your code and include words like "java" or "oracle" too. The same applies if you create new tags.
To circumvent this limitation (and have some fun!!) you could try to implement a learning algorithm. Learning algorithms are those who refine their outcome the more you use them (so they indeed... learn!). Some algorithm requires initial training (many spam filters and voice recognition programs need this initial "primer"). Some don't.
I am absolutely no expert on the subject, but two common AI's are: the Naive Bayes Classifier and some flavour of Neural network.
Although the WP pages might look scary, they are surprisingly easy to implement (at least in Python). Here's the recording of a lecture at PyCon 2009 on the subject "Easy AI with Python". I found it very informative and even somehow inspiring! :)
HTH!
You should have a look at this post :
Any suggestions for a db schema for storing related keywords?
If you're looking for a schema for storing related tags it will help.
Relevancy searches where multiple agents play a part are usually done using Collaborative filtering. You might want to give that a look see.
Look up Clustering (Machine Learning algorithm). Don't be intimidated by math, it's a pretty straightforward algorithm. Check out Machine Learning for Hackers for simpler explanations of many Machine Learning algorithms and methods.
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'm curious what the programming terms or methodology is used when Google shows you the "did you mean" link for a word that is made up of multiple words?
For example if I type in "redflower.jpg" It knows to break that up into Red Flower
Is there a common paradigm for doing that sort of operation? Would a Lucene search give you that?
thanks!
If google does not see a lot of matching results for reflowers.jpg, it might then try to cut the words in multiple words until it finds a lot of matching results.
It might also recognize the extension (.jpg), recognize the image extension and then try to find images with the similar name.
If I would have to make an algorithm like this, I would use an huge EXISTING database (either a dictionary or a search engine) and then try what I said in the beginning of my post.
Perhaps they could to look at what other people do when they have searched for redflowers.jpg? Maybe a number of people searched for "redflowers.jpg", didn't click on any links, and then searched for "Red Flower" and found some results worth clicking on.
Of course they would have to take into account that the queries are similar (contain matching strings), otherwise some strange results might appear.