I am wondering what algorithm would be clever to use for a tag driven e-commerce enviroment:
Each item has several tags. IE:
Item name: "Metallica - Black Album CD", Tags: "metallica", "black-album", "rock", "music"
Each user has several tags and friends(other users) bound to
them. IE:
Username: "testguy", Interests: "python", "rock", "metal", "computer-science"
Friends: "testguy2", "testguy3"
I need to generate recommendations to such users by checking their interest tags and generating recommendations in a sophisticated way.
Ideas:
A Hybrid recommendation algorithm can be used as each user has friends.(mixture of collaborative + context based recommendations).
Maybe using user tags, similar users (peers) can be found to generate recommendations.
Maybe directly matching tags between users and items via tags.
Any suggestion is welcome. Any python based library is also welcome as I will be doing this experimental engine on python language.
1) Weight your tags.
Tags fall into several groups of interest:
My tags that none of my friends share
Tags a number of my friends share, but I don't
My tags that are shared by a number of my friends.
(sometimes you may want to consider friend-of-a-friend tags too, but in my experience the effort hasn't been worth it. YMMV.)
Identify all tags that the person and/or the person's friends have in interests, and attach a weight to the tags for this individual. One simple possible formula for tag weight is
(tag_is_in_my_list) * 2 + (friends_with_tag)/(number_of_friends)
Note the magic number 2, which makes your own opinion worth twice as much as that of all of your friends put together. Feel free to tweak :-)
2) Weight your items
For each item that has any of the tags in your list, just add up all of the weighted values of the tags. A higher value = more interest.
3) Apply a threshold.
The simplest way is to show the user the top n results.
More sophisticated systems also apply anti-tags (i.e. topics of non-interest) and do many other things, but I have found this simple formula effective and quick.
If you can, track down a copy of O'Reilly's Programming Collective Intelligence, by Toby Segaran. There's a model solution in it for exactly this problem (with a whole bunch of really, really good other stuff).
Your problem is similar to product recommendation engines, such as Amazon's well publicized site. These use a learning algorithm called association rules, which basically build a conditional probability of user X buying product Y based on common features Z between the user and product. A lot of open source toolkits implement association rules, such as Orange and Weka.
You can use the Python Semantic module for Drools to specify your rules in python scripting language. You can accomplish this easily using Drools. It is a terrific rules engine that we used to solve several recommendation engines.
I would use a Restricted Boltzmann Machine. Gets around the problem of similar but not identical tags quite neatly.
Related
I am new at the idea of programming algorithms. I can work with simplistic ideas, but my current project requires that I create something a bit more complicated.
I'm trying to create a categorization system based on keywords and subsets of 'general' categories that filter down into more detailed categories that requires as little work as possible from the user.
I.E.
Sports >> Baseball >> Pitching >> Nolan Ryan
So, if a user decides they want to talk about "Baseball" and they filter the search, I would like to also include 'Sports"
User enters: "baseball"
User is then taken to Sports >> Baseball
Now I understand that this would be impossible without a living - breathing dynamic program that connects those two categories in some way. It would also require 'some' user input initially, and many more inputs throughout the lifetime of the software in order to maintain it and keep it up to date.
But Alas, asking for such an algorithm would be frivolous without detailing very concrete specifics about what I'm trying to do. And i'm not trying to ask for a hand out.
Instead, I am curious if people are aware of similar systems that have already been implemented and if there is documentation out there describing how it has been done. Or even some real life examples of your own projects.
In short, I have a 'plan' but it requires more user input than I really want. I feel getting more info on the subject would be the best course of action before jumping head first into developing this program.
Thanks
IMHO It isn't as hard as you think. What you want is called Tagging and you can do it Automatically just by setting the correlation between tags (i.e. a Tag can have its meaningful information plus its reation with other ones. Then, if user select a Tag well, you related that with others via looking your ADT collection (can be as simple as an array).
Tag:
Sport
Related Tags
Football
Soccer
...
I'm hoping this helps!
It sounds like what you want to do is create a tree/menu structure, and then be able to rapidly retrieve the "breadcrumb" for any given key in the tree.
Here's what I would think:
Create the tree with all the branches. It's okay if you want branches to share keys - as long as you can give the user a "choice" of "Multiple found, please choose which one... ?"
For every key in the tree, generate the breadcrumb. This is time-consuming, and if the tree is very large and updating regularly then it may be something better done offline, in the cloud, or via hadoop, etc.
Store the key and the breadcrumb in a key/value store such as redis, or in memory/cached as desired. You'll want every value to have an array if you want to share keys across categories/branches.
When the user selects a key - the key is looked up in the store, and if the resulting value contains only one match, then you simply construct the breadcrumb to take the user where you want them to go. If it has multiple, you give them a choice.
I would even say, if you need something more organic, say a user can create "new topic" dynamically from anywhere else, then you might want to not use a tree at all after the initial import - instead just update your key/value store in real-time.
I got a financial application and I wish to add to it the ability to get user command or input in textbox and then take the right action. for example, wish the user to write "show the revenue in the last 10 days" and it'll show the revenue to him/her - the point is that I wish it to really understand the meaning of the question, so the previus statement will bring the same results as "do I got any revenue in the last 10 days" or something like that - BI (something like the Wolfram|Alpha engine).
I wonder if there's any opensource library or algorithm books or whatever that I can use to learn the subject. Regards to opensource libraries - I don't mind which language it'll be written in.
I've read about this subject and saw many engines and services (OpenNLP, Apache UIMA, CoreNLP etc.) but did not figure out if they're right for my needs.
Any answer or suggestion is welcome.
Many thanks!
The field you're talking about is usually called "natural language processing". It's hard, and an active field of research. There are various libraries which you could consider based on your preferred programming language and use case:
http://en.wikipedia.org/wiki/List_of_natural_language_processing_toolkits
I've used NLTK a little bit. This field is seriously difficult to get right, so you might want to try to restrict your application to some small set of verbs and nouns such that people are using a controlled vocabulary in the first instance, and then try to extend it beyond that.
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.
this is a more generic question:
I am trying to find a way to classify a segment of data that have overlapping or sometimes multiple "broad categories."
I try to use "tags" as a way to allow some granularity, but I don't have a way to convey the more "top level" categories for people to browse.
For example, one of the vendors I am putting trying to classify does "Information Management" which isn't really a category of its own, it supports "Business Intelligence" but it isn't really Business Intelligence.
Not sure if people have thoughts or is the way to just look at how other people categorize these vendors in like directories...but wanted to know if poeple knew of both a way and, in particular, a presentation-layer / UI that makes it easier to "connect the dots" so to speak.
NOTE: This thread is a subset of what I am asking so additional elaborations would helpful:
Most elegant UI for categorizing items?
A more concrete direction: a non-proprietary way to create relationships like this:
http://www.thebrain.com/
A tag cloud, is the most ubiquitous way of displaying top level tags. Depending on your use they may be highlighted by popularity, or some other metric determined by a Folksonomy. Think Flickr or Del.icio.us