I have a group of documents in MongoDB with a "description" value about the size of a tweet. I need to generate a trending topics list from this. Clearly this is a solved problem but I can't find a definitive answer/gem for getting the job done without writing the code myself.
I am using ruby & mongoid in my app.
Is there any ruby gem that will help with or handle this? Thanks.
I know of no such gem, but here's an algorithm you may write for yourself:
Extract n-grams from texts. Since texts are small (tweet size you said) extract all n-grams, no limit here.
"I eat icecream" => {(I), (eat), (icecream), (I eat), (eat icecream), (I eat icecream)}
Compute TF-IDF weight vectors for each text's n-grams
{(I):0.1, (eat):0.01, (icecream):0.2, (I eat):0.12, (eat icecream):0.001, (I eat icecream):0.00012}
Use cosine similarity as a measure function for a incremental clustering algorithm over your vectors, maybe script the Weka library over JRuby
Order all clusters by the population size. The n-grams in the centers of largest clusters are your trendy topics.
A quick search of rubygems.org revelead that you are going to have to do some programming. This is a good thing as a system to generically detect trends would either be hopelessly difficult to setup and tune or awful at guessing what dictates a "trend" in your application.
I'm going to make some assumptions about your application.
Let's assume users are self categorizing their tweets by using hash tags (#). Also, lets go ahead and say a sorted count of these hash tags would determine if a topic was trending.
Now let's talk about the computer science part. Given our assumptions above, you will need to be able to quickly query and sort a collection of hashtags to figure out what is trending.
Your are using MongoDB and mongoid (with rails) so the simplest way to do this would be to create a collection that has tag documents that contain a count of their use. Create indexes on tag and count.
When someone tweets, figure out what the hash tags are, look them up in the tags collection and increment their count. To figure out what is trending, query the tags collection and sort by count. This would get you all-time trending hash tags.
If you wanted to get more specific, instead of just storing counts, store counts broken out by time deltas (week, day, hour etc) perhaps storing them separately. You could create documents that represent your time delta instead of the individual tags and store all the tags with their counts inside.
{
start: "start datetime",
end: "end datetime",
tags: {
awesome: 3,
cool: 2,
boring: 2
}
}
You could also use a capped collection. Hope that helps, all of this really depends on what you are trying to do. You can get really crazy and calculate the trends with time decay, etc. You could read the reddit or hacker news code to get a good idea of what that is like.
Related
I have an app that displays information about certain venues. Each venue is awarded a rating on a scale from 0-100. The app includes a map, and on the map I'd like to show the best nearby venues. (The point is to recommend to the user alternative venues that they might like.)
What is the best way to approach this problem?
If I fetch the nearest x venues, many bad venues (i.e. those with a
low rating) show.
If I fetch the highest rated venues, many of them
will be too far away to be useful as recommendations.
This seems like a pretty common challenge for any geolocation app, so I'm interested to know what approach other people have taken.
I have considered "scoring" each possible venue by taking into account its rating and its distance in miles.
I've also considered fetching the highest rated venues within a y mile radius, but this gets problematic because in some cities there are a lot of venues in a small area (e.g. New York) and in others it's reasonable to recommend venues that are farther away.
(This is a Rails app, and I'm using Solr with the Sunspot gem to retrieve the data. But I'm not necessarily looking for answers in code here, more just advice about the logic.)
Personally, I would implement a few formulas and use some form of A/B testing to get an idea as to which ones yield the best results on some outcome metric. What exactly that metric is is up to you. It could be clicks, or it could be something more complicated.
Start out with the simplest formula you can think of (ideally one that is computationally cheap as well) to establish a baseline. From there, you can iterate, but the absolute key concept is that you'll have hard data to tell you if you're getting better or worse, not just a hunch (perhaps that a more complicated formula is better). Even if you got your hands on Yelp's formula, it might not work for you.
For instance, as you mentioned, a single score calculated based on some linear combination of inverse distance and establishment quality would be a good starting point and you can roll it out in a few minutes. Make sure to normalize each component score in some way. Here's a possible very simple algorithm you could start with:
Filter venues as much as possible on fast-to-query attributes (by type, country, etc.)
Filter remaining venues within a fairly wide radius (you'll need to do some research into exactly how to do this in a performant way; there are plenty of posts on Stackoverflow and else where on this. You'll want to index your database table on latitude and longitude, and follow a number of other best practices).
Score the remaining venues using some weights that seem intuitive to you (I arbitrarily picked 0.25 and 0.75, but they should add up to 1:
score = 0.25*(1-((distance/distance of furthest venue in remaining
set)-distance of closest venue)) + 0.75*(quality score/highest quality
score in remaining set)
Sort them by score and take the top n
I would put money on Yelp using some fancy-pants version of this simple idea. They may be using machine learning to actually select the weights for each component score, but the conceptual basis is similar.
While there are plenty of possibilities for calculating formulas of varying complexity, the only way to truly know which one works best is to gather data.
I would fix the number of venues returned at say 7.
Discard all venues with scores in the lowest quartile of reviewers scores, to avoid bad customer experiences, then return the top 7 within a postcode. If this results in less than 7 entries, then look to the neighboring post codes to find the best scores to complete the list.
This would result in a list of top to mediocre scores locally, perhaps with some really good scores only a short distance away.
From a UX perspective this would easily allow users to either select a postcode/area they are interested in or allow the app to determine its location.
From a data perspective, you already have addresses. The only "tricky" bit is determining what the neighboring postcodes/areas are, but I'm sure someone has figured that out already.
As an aside, I'm a great believer in things changing. Like restaurants changing hands or the owners waking up and getting better. I would consider offering a "dangerous" list of sub-standard eateries "at your own risk" as another form of evening entertainment. Personally I have found some of my worst dining experiences have formed some of my best dining out stories :-) And if the place has been harshly judged in the past you can sometimes find it is now a gem in the making.
First I suggest that you use bayesian average to maintain an overall rating for all the venues, more info here: https://github.com/tyrauber/acts_rateable
Then you can retrieve the nearest venues ordered by distance then ordered by rating. two order by statements in your query
Hello stackoverflow folks,
We got a Rails project which is growing and growing and we now get first performance problems on the search, because we don't know how to utilize sphinx properly for our needs.
We have search queries like "Java PHP Software developer". Our problem is now the ranking should work with multiple things.
As search fields we have tag list, description and title.
If one of the terms is inside of one of the fields it should get for example 2 points. More Points if its in more fields, but not multiple points if it is in the same field more than once.
Next Problem is I have a big file with synonyms for which should also be checked. It looks like this:
Java > Java
Java-EE > Java
...
So if Java-EE is found it should get some points too but with a penalty for being a synonym.
Maximum amount of points would be 5 as in 5 stars which get displayed.
Any speedy solution would be nice because at the moment it's done in plain ruby and it gets slow, because we cant rank properly in sphinx.
If there is a solution with another search engine that would also be very nice, as it could be changed.
Thanks in advance for all efforts. All spelling corrections and questions to clear the question are welcome.
Most of the performance issues can be solved by changing the way you use sphinx. First you need to address how you index the data in sphinx. Doing some processing during while indexing will make the search quicker and the results more relevant. Second, tackle the search terms and last but not least, decide on the ranking algorithm to use.
I am going to use the "title" field as an example, but the logic can be replicated for all fields.
Indexing
Add two fields to sphinx ("title" and "title_synonyms"). For each record in the database do the following :-
Perform a DISTINCT on the words to remove duplicates ("Ruby Developer / Java Developer" will become "Ruby Developer / Java". This will stop records from getting two scores for duplicates when searching. This goes in to "title"
Take the DISTINCT title from above and REPLACE all the words with their expanded synonym equivalents. I would suggest putting the synonyms in the DB to make the expansion easier. The text would then become "Ruby Developer / Java-EE". Each word must be replaced with all the synonyms. If Java has two synonyms, they both must be in the field. This goes into "title_synonyms"
Searching
Because there are now two fields in sphinx we can give them each a different weight; "title" can get a weight of "10" and "title_synonyms" a weight of "3". That means a record has to match 4 synonyms before it ranks higher than one with the original title. You can play around with the weights to suit your needs.
Lets assume a user was searching for "Java Developer". For the search phrase do the following :-
Remove duplicate words
Get synonyms for each word in the search phrase
Set Matching Mode in Sphinx to SPH_MATCH_EXTENDED
The above rules will mean the search in sphinx looks like this :-
#title "Java Developer" | #title_synonyms "Java-EE"
If you want to rank exact matches higher than lexemes, the search query would look like this :-
#title ("Java Developer" | "=Java =Developer") | #title_synonyms ("Java-EE" | "=Java-EE")
You will need to use SPH_RANK_PROXIMITY_BM25 or SPH_RANK_SPH04 to make this work properly though.
Ranking
You can try any of the built in ranking algorithms to see what the results look like. I recommend SPH_RANK_MATCHANY or SPH_RANK_WORDCOUNT as a start.
For Proximity and exact match ranking use SPH_RANK_PROXIMITY_BM25, SPH_RANK_SPH04 or SPH_RANK_EXPR where you can use your own algorithm.
Conclusion
You should now have a search that is both fast and accurate. Very little work has to be done by your Ruby application and most of the work is done inside sphinx (where it should be).
Hope this helps...
This performance problem is an algorithm problem.
If you cannot express the problem in a way to utilize a backend tool, like sphinx or the database engine, then you are doing the processing in ruby, and that's easy to have a performance problem.
First, do as much as you can with sphinx (or whatever other search engine) and the database as you can. The more pre-digested the data coming into ruby, the less you have to do in ruby code, and that will likely be faster, since databases have been highly optimized over the last half century.
So, for example, run sphinx on the key words. Also run sphinx on the synonyms. Limit all the answers to the top results, and merge the results. That way your ruby code will be limited to the likely high results instead of having to consider the whole database of entries.
Once in ruby, the most important thing is to avoid high order algorithms, that is, make sure you are using a low order algorithm.
As you process your raw data, if you hold your top results in an array and try to sort or scan the array, you are going to have an N-squared order. That is, your order will be the product of the number of raw entries and the number of elements you keep in your array.
The best algorithms for your problem are a priority queue implemented by a heap like container, or a b-tree. Both have N-log-N order (N times the log of N), or the number of raw data records time the log of the number of items you will keep in your container.
A heap is a binary tree, where each node in the tree (not just the leaves but each node) has a rated record. The nodes below each record all have lower ranks. This is called the heap condition.
There are algorithms for adding elements, taking the top ranked element out, and replacing the lowest ranked element which maintain the heap condition. Look up binary heap in the wikipedia.
Let's say your site will display the top 100 ranked results. Maintain a help where the root is the lowest ranked. Populate the heap by adding the first 100 raw records you are processing.
Now for record 101 and after, compare its rank with the root. If the new record is ranked higher, use the delete algorithm to reduce your heap to 99 nodes (which will remove the lowest ranked record in the heap) and add your new record to the heap.
Once you have gone through all your records, you will have the top 100 ranked results. The heap delete algorithm will pull them out in reverse order.
It's known how collaborative filtering (CF) is used for movie, music, book recommendations. In the paper 'Collaborative Topic Modeling for Recommending Scientiļ¬c Articles' among other things authors show an example of collaborative filtering applied to ~5,500 users and ~17,000 scientific articles. With ~200,000 user-item pairs, the user-article matrix is obviously highly sparse.
What if you do collaborative filtering with matrix factorization for, say, all news articles shared on Twitter? The matrix will be even sparser (than that in the scientific articles case) which makes CF not very applicable. Of course, we can do some content-aware analysis (taking into account, the text of an article), but that's not my focus. Or we can potentially limit our time window (focus, say, on all news articles shared in the last day or week) to make the user-article matrix denser. Any other ideas how to fight the fact that the matrix is very sparse? What are the results in research in the area of CF for news article recommendations? Thanks a lot in advance!
You might try using an object-to-object collaborative filter instead of a user-to-object filter. Age out related pairs (and low-incidence pairs) over time since they're largely irrelevant in your use case anyway.
I did some work on the Netflix Prize back in the day, and quickly found that I could significantly outperform the base model with regard to predicting which items were users' favorites. Unfortunately, since it's basically a rank model rather than a scalar predictor, I didn't have RMSE values to compare.
I know this method works because I wrote a production version of this same system. My early tests showed that, given a task wherein 50% of users' top-rated movies were deleted, the object-to-object model correctly predicted (i.e., "replaced") about 16x more of users' actual favorites than a basic slope-one model. Plus the table size is manageable. From there it's easy to include a profitability weight against the sort order, etc. depending on your application.
Hope this helps! I have a working version in production but am still looking for beta clients to bang on the system... if anyone has time to give it a run I'd love to hear from you.
Jeb Stone, PhD
www.selloscope.com
Lucene does not support it out of the box, so I need some help building my query.
Lets say I have the document with a field value "Develop"
I would like this document to be returned for the searches "Dev" and "lop".
Maybe creating two queries?
"*keyword"
and
"keyword*"
and
"keyword"
?
How would you go about doing this with multiple words? Would you split the sentence/search into a words list and do the previous example for each word?
What you're asking is if I understand you correctly not feasible on any large scale search engine.
Lucene creates an index over keywords using term-document matrix and inverted-file techniques (see links at the bottom). A fully fledged string matching might be very nice to have, but it does not scale: you will never be able to query a decently sized index (say more than a couple of dozen/hundreds of documents) in an acceptable time.
Still, here are two ideas that might help...
Syllable tokenization
To come back to your example with 'Develop'. As long as you are happy with letting users search for syllables I guess you can do something.
You would have to create use tokenizer that splits up words in your indexed according to their syllables and create a database index over the syllables. (I am not sure there are built in tokenizers for the English language that can do that and writing one on your own might be tricky...)
An important thing to note:
If you would index the full words AND the seperate syllables the size of your index will be much larger than if you only index one of the two.
However I would not suggest to index only syllables. If you want to also allow your users to search for the full word 'Develop' (which I guess you want) this would result in two queries with a logical and between them, namely <'dev' AND 'lop'>. Although Lucene supports such logical constructs in queries they are very expensive. I have personally had some trouble in the past using logical queries in Lucene.
Stemming
Another way to somehow arrive at what you're trying could be to use a brutal form of word stemming (http://en.wikipedia.org/wiki/Stemming) that stems words to their first syllable. (This would allow to search for 'dev' but not for 'lop'...)
Again, I don't think such a word stem feature is already in Lucene. Writing one for yourself will be a pain and involve working with/importing huge dictionaries.
Links
These might be looking into if you don't know about search engine internals:
http://en.wikipedia.org/wiki/Index_%28search_engine%29
http://en.wikipedia.org/wiki/Vector_space_model
http://en.wikipedia.org/wiki/Inverted_file
http://en.wikipedia.org/wiki/Term-document_matrix
http://en.wikipedia.org/wiki/Tf-idf
Consider the following site:
http://maps.google.com
It has a main text input, where the user can type business, countries, provinces, cities, addresses and zip codes. I wonder which is the best way to implement a search like this. I realize that probably Google Maps uses a full text search with all kinds of data in the same table, and it has a chance of having a parser which classifies the input (i.e. between numeric, like zip codes and coordinates, and textual, like business and addresses).
With the data spread in many tables and systems, a parser is essential. The parser could be built from regular expressions, or could be built with IA tools like Artificial Neural Networks and Genetic Algorithms.
Which approach would you recommend?
It might be best to aggregate the data from all of your tables into a search index. Lucene is a free search engine, similar to how Google's search engine works (inverted index), and it should allow you to search by any of those values or any combination of them with relative ease.
http://lucene.apache.org/java/docs/
Lucene comes with its own query language (again, very similar to Google's or any other Internet search sites syntax). The only drawback of using something like Lucene is you would need to build its index. You wouldn't be querying your database directly (which could get very complicated...inverted index are pretty much designed for what your trying to do), so you need to periodically gather up new information from your database and add it to your index. It might also be necessary to rebuild your index to remove unneeded data.
With Lucene, you get a pretty flexible query syntax that most people are familiar with (because pretty much everyone searches the internet), it performs very well, and is not terribly complicated. By using Lucene, you avoid the hit of using regular expressions (which are not the most performant text searching mechanism), and you don't have to write your own parser. Should be a win-win, aside from a little learning curve to build a Lucene index generator and figure out how to query that index.
I'd have the data in one database. If the data got to big or I knew it would be huge, I'd assign an id to each business, address etc, then have other tables which reference this data.
Regular Expressions would only be necessary if the user could define what they want to search for:
business: Argos
But then what happens if they want an Argos in Manchester (Sorry, I'm English), maybe then get the location of the user based on their IP but what happens if they say:
business: Argos Scotland
Now you don't know if the company has two words, or if there is a location next to it. All of this has to be taken into consideration.
P.s Sorry if that made no sense.
You will need to pre process the query before doing a full text search on it. If you are using a GIS database, then you will already have columns like city, areacode, country etc. Convert your query into tokens seperated on space or commas, or both. Then hit individual columns to see match. This way you will know what part of the query is the city, the areacode etc.
You could also try some naive approximation approaches,example - 6 consecutive numbers will probably be an area code. Look for common words like "road" , "restaurant" , "street" etc which will be part of many queries and then use some approximation to figure out what they are looking for. Hope this helps.