New to this and a long time since I've done any programming or forums ....
However, this is really getting under my skin.
I've been looking around at the algorithms used for Amazon etc on the recommendations they make around products which have an affinity to the ones people have selected - clearly this works very well.
Here is what I am wondering....
A - why would this be limited to affinity? Is there never a situation where a product would be exclusive of the original selection and perhaps a parallel but not like product might make sense?
B - why would a neural network not make sense? Could this not work well to provide a good link or would you just end up with a few product which have a very low weighting and therefore perpetuates their non selection?
Thanks for your views.
James
Question A: You do not need to limit it to affinity. However, you do need to "package up" all other pertinent information in a way that you can present it to the algorithm. You should read up on "association rules", "frequent itemsets" & recommender algorithms. Most of these algorithms analyze transaction data and learn rules like {peanuts, hot dogs} = {beer}. As to going away from affinity you can produce multiple sets where you reduce beer to {alcoholic beverage} then use multiple frequent item sets, at different levels of specificity, and then some sort of ensemble algorithm to combine them.
Question B: A neural network, or any other similar model would not work due to the dimensionality. Say you are Amazon.com and you want to input an item set of up to 5 items. You might encode that as an input neuron count equal to one neuron for each item times 5. How many items are on Amazon. I have no idea, but I am guessing its over 100k. Even if you get creative with dimensionality reduction, this is going to be a MASSIVE neural network, or even support vector machine or random forest.
Related
Hospitals are changing the way they sterilize their equipment. Previously the local surgeons kept all their own equipment and made their own surgery trays. Now they have to confine to a country wide standard. They want to know how many of the new trays they can make from their existing stock, and how much new equipment they need to buy.
The inventory of medical equipment looks like this:
http://pastebin.com/rstWSurU
each hospitals has codes for various medical equipment and then a number for how many they have of the corresponding item
3 surgery trays with their corresponding items are show in this dictionary.
http://pastebin.com/bUAZhanK
There are a total of 144 different operation trays
the hospitals will be told they need 25 of tray x, 30 of tray y, etc...
They would like to maximize the amounts of trays they can finish with their current stock. They would also like to know what equipment they need to purchase in order to finish the remaining trays.
I have thought about two possible solutions one being representing the problem as a linear programming problem. The other solving the problem by doing a round-robin brute force solve of the first 90% of the problem and solving the remaining 10% by doing a randomized algorithm several times and then pick the best solve of those tries.
I would love to hear if anyone knows a smart way of how to tackle this problem!
If I understand this correctly we can optimize for each hospital separately. My guess is that the following would be a good start for an MIP (Mixed Integer Programming) model:
I use the following indices: i is items and t is trays. x(t,i) indicates how many items we assign to each tray type. y(t) counts the number of trays of each type that we can compose using the available items. From the solution we can calculate the shortages that we need to order.
Of course we are just maximizing the number of trays we can make. There is no consideration of balancing (many trays of one type and few or zero of another). I mitigate a little bit by not allowing to create more trays than required (if we have more items they need to go to other types of trays). This requirement is formulated as an upper bound on y(t).
For large problems we can restrict the (t,i) combinations to the ones that are possible. This will make the model smaller. When using precise math notation:
A further optimization would be to substitute out the variables x(t,i).
Adding shipping surplus items to other hospitals would make the model more difficult. In that case we could end up with a model that needs to look at all hospitals simultaneously. May be an interesting case for some decomposition approach.
I'm attempting to write some code for item based collaborative filtering for product recommendations. The input has buyers as rows and products as columns, with a simple 0/1 flag to indicate whether or not a buyer has bought an item. The output is a list similar items for a given purchased, ranked by cosine similarities.
I am attempting to measure the accuracy of a few different implementations, but I am not sure of the best approach. Most of the literature I find mentions using some form of mean square error, but this really seems more applicable when your collaborative filtering algorithm predicts a rating (e.g. 4 out of 5 stars) instead of recommending which items a user will purchase.
One approach I was considering was as follows...
split data into training/holdout sets, train on training data
For each item (A) in the set, select data from the holdout set where users bought A
Determine which percentage of A-buyers bought one of the top 3 recommendations for A-buyers
The above seems kind of arbitrary, but I think it could be useful for comparing two different algorithms when trained on the same data.
Actually your approach is quiet similar with the literature but I think you should consider to use recall and precision as most of the papers do.
http://en.wikipedia.org/wiki/Precision_and_recall
Moreover if you will use Apache Mahout there is an implementation for recall and precision in this class; GenericRecommenderIRStatsEvaluator
Best way to test a recommender is always to manually verify that the results. However some kind of automatic verification is also good.
In the spirit of a recommendation system, you should split your data in time, and see if you algorithm can predict what future buys the user does. this should be done for all users.
Don't expect that it can predict everything, a 100% correctness is usually a sign of over-fitting.
I was working on a algorithm, where I am given some input and I am given output for them, and given the output for 3 months (give or take) I need a way to find/calculate what might be the future output.
Now, this problem given can be related to stock exchange, we are given certaing constraints and certain outcomes, and we need to find the next.
I stumbled upon neural network stock market prediction, you can Google it, or you can read about it here, here and here.
To get started at making the algorithm, I couldn't figure out what should be the structure of layers.
The given constraint are:
The output would always be integer.
The output would always be between 1 and 100.
There is no exact input for say, just like stock market, we just know that the stock price would fluctuate btw 1 and 100, so we might (or not?) consider this as the only input.
We have record for last 3 months (or more).
Now, my first question is, how many nodes do I take for input?
The output is just one, fine. But as I said, should I take 100 nodes for input layer (given that the stock price would always be integer and would always be btw 1 and 100?)
What about hidden layer? How many nodes there? Say, if I take 100 nodes there too, I don't think that would train the network much, because what I think is that for each input we need to take into account all previous input also.
Say, we are calulating output for 1st day of 4th month, we should have 90 nodes in hidden/middle layer (imagining each month is 30 days for simplicity). Now there are two cases
Our prediction was correct and outcome was same as we predicted.
Our prediction failed, and the outcome was different than what we predicted.
Whatever the case be, now when we are calculating the output for 2nd day of 4th month, we need not only those 90 input(s) but also that last result (and not the prediction, be it the same!) too, so we now have 91 nodes in our middle/hidden layer.
And so on, it would keep increasing the number of nodes each day, AFAICT.
So, my other question is how do I define/set the number of nodes in hidden/middle layer if its dynamically changing.
My last question is, is there any other particular algorithm out there (for this kinda thing/stuff) that I am not aware of? That I should be using instead of messing around with this neural networking stuff?
Lastly, is there anything, that I might be missing that might cause me (rather the algo I am making) to predict the output, I mean any caveats, or anything that might make it go wrong that I might be missing?
There is much to tell as an answer to your question. In fact, your question addresses the problem of time series forecasting in general, and neural networks application for this task. I'm writing here only several most important keys, but after reading this you should possibly dig into Google's results for the query time series prediction neural network. There is a lot of works where the principles are covered in details. A variety of software implementations (with source codes) do also exist (here is just one of examples with codes in c++).
1) I must say that the problem is 99% about data preprocessing and choosing correct input/output factors, and only 1% about concrete instrument to use, whether neural networks or something other. Just as a side note, neural networks can internally implement most of other data analysis methods. For example, you can use neural network for Principal Component Analysis (PCA) which is closely related to SVD, mentioned in another answer.
2) It's very rare that input/output values are strictly fit a specific region. Real life data can be considered as unbounded in absolute values (even if its changes seem producing a channel, it can be broken down just in a moment), but neural network can operate in a stable conditions only. This is why the data is normally converted into increments first (by calculating deltas between i-th point and i-1, or taking log from their ratio). I suggest you do it with your data anyway, though you declare it's inside [0, 100] region. If you don't do it, neural network will most likely degenerate to a so called naive predictor which produce a forecast with each next value equal to previous.
The data then is normalized into [0, 1] or [-1, +1]. The second is appropriate for the case of time series prediction where +1 denotes move up, and -1 - move down. Use hypertanh activation function for neurons in your net.
3) You should feed NN with an input data obtained from a sliding window of dates. For example, if you have a data for a year and every point is a day, you should choose the size of window - say, a month - and slide it day by day, from the past to the future. The day just at the right bound of the window is the target output for NN. This is a very simple approach (there are much more complicated), I mention it just because you ask how to handle data which does continuously arrive. The answer is - you don't need to change/enlarge your NN every day. Just use a constant structure with a fixed window size and "forget" (do not provide to the NN) the oldest point. It's important that you do not treat all the data you have as a single input, but divide it into many small vectors and train NN on them, so the net can generalize data and find regularity.
4) The size of sliding window is your NN input size. The output size is 1. You should play with hidden layer size to find better performance. Start with a value which somethat between input and output, for example sqrt(in*out).
According to lastest researches, Recurrent Neural Networks seem operating better for tasks of time series forecasting.
I agree with Stan when he says
1) I must say that the problem is 99% about data preprocessing
I've applied Neural Networks for 25+ years to various aerospace applications including helicopter flight control - setting up the input/output data set is everything - all else is secondary.
I'm amazed, in smirkman's comment that Neural Networks were quickly dropped "as they produced nothing worthwhile" - that tells me that whoever was working with Neural Networks had little experience with them.
Given that the topic discusses neural network stock market prediction - I'll say that I've made it work. Test results are downloadable from my website at www.nwtai.com.
I don't give away how it was done but there's enough interesting data that should make you want to explore using Neural Networks more seriously.
This kind of problem was particularly well researched by thousands of people who wanted to win the 1M$ NetFlix prize.
Earlier submissions were often based on K Nearest Neigbours. Later submissions were made using Singular Value Decomposition, Support Vector Machines and Stochastic Gradient Descent. The winner used a blend of several techniques.
Reading the excellent Community forums will give you many insights about the best methods to predict the future from the past. You'll also find loads of source code for the different methods.
Amusingly, neural networks were quickly dropped, as they produced nothing worthwhile (and I personally have yet to see a non-trivial NN produce anything of value).
If you are starting out, I'd suggest SVD as a first path; it's quite easy to make and often produces surprising insights into data.
Good luck!
I'm a computer science student and for this years project, I need to create and apply a Genetic Algorithm to something. I think Neural Networks would be a good thing to apply it to, but I'm having trouble understanding them. I fully understand the concepts but none of the websites out there really explain the following which is blocking my understanding:
How the decision is made for how many nodes there are.
What the nodes actually represent and do.
What part the weights and bias actually play in classification.
Could someone please shed some light on this for me?
Also, I'd really appreciate it if you have any similar ideas for what I could apply a GA to.
Thanks very much! :)
Your question is quite complex and I don't think a small answer will fully satisfy you. Let me try, nonetheless.
First of all, there must be at least three layers in your neural network (assuming a simple feedforward one). The first is the input layer and there will be one neuron per input. The third layer is the output one and there will be one neuron per output value (if you are classifying, there might be more than one f you want to assign a "belong to" meaning to each neuron).. The remaining layer is the hidden one, which will stand between the input and output. Determining its size is a complex task as you can see in the following references:
comp.ai faq
a post on stack exchange
Nevertheless, the best way to proceed would be for you to state your problem more clearly (as weel as industrial secrecy might allow) and let us think a little more on your context.
The number of input and output nodes is determined by the number of inputs and outputs you have. The number of intermediate nodes is up to you. There is no "right" number.
Imagine a simple network: inputs( age, sex, country, married ) outputs( chance of death this year ). Your network might have a 2 "hidden values", one depending on age and sex, the other depending on country and married. You put weights on each. For example, Hidden1 = age * weight1 + sex * weight2. Hidden2 = country * weight3 + married * weight4. You then make another set of weights, Hidden3 and Hidden4 connecting to the output variable.
Then you get a data from, say the census, and run through your neural network to find out what weights best match the data. You can use genetic algorithms to test different sets of weights. This is useful if you have so many edges you could not try every possible weighting. You need to find good weights without exhaustively trying every possible set of weights, so GA lets you "evolve" a good set of weights.
Then you test your weights on data from a different census to see how well it worked.
... my major barrier to understanding this though is understanding how the hidden layer actually works; I don't really understand how a neuron functions and what the weights are for...
Every node in the middle layer is a "feature detector" -- it will (hopefully) "light up" (i.e., be strongly activated) in response to some important feature in the input. The weights are what emphasize an aspect of the previous layer; that is, the set of input weights to a neuron correspond to what nodes in the previous layer are important for that feature.
If a weight connecting myInputNode to myMiddleLayerNode is 0, then you can tell that myInputNode is not important to whatever feature myMiddleLayerNode is detecting. If, though, the weight connecting myInputNode to myMiddleLayerNode is very large (either positive or negative), you know that myInputNode is quite important (if it's very negative it means "No, this feature is almost certainly not there", while if it's very positive it means "Yes, this feature is almost certainly there").
So a corollary of this is that you want the number of your middle-layer nodes to have a correspondence to how many features are needed to classify the input: too few middle-layer nodes and it will be hard to converge during training (since every middle-layer node will have to "double up" on its feature-detection) while too many middle-layer nodes may over-fit your data.
So... a possible use of a genetic algorithm would be to design the architecture of your network! That is, use a GA to set the number of middle-layer nodes and initial weights. Some instances of the population will converge faster and be more robust -- these could be selected for future generations. (Personally, I've never felt this was a great use of GAs since I think it's often faster just to trial-and-error your way into a decent NN architecture, but using GAs this way is not uncommon.)
You might find this wikipedia page on NeuroEvolution of Augmenting Topologies (NEAT) interesting. NEAT is one example of applying genetic algorithms to create the neural network topology.
The best way to explain an Artificial Neural Network (ANN) is to provide the biological process that it attempts to simulate - a neural network. The best example of one is the human brain. So how does the brain work (highly simplified for CS)?
The functional unit (for our purposes) of the brain is the neuron. It is a potential accumulator and "disperser". What that means is that after a certain amount of electric potential (think filling a balloon with air) has been reached, it "fires" (balloon pops). It fires electric signals down any connections it has.
How are neurons connected? Synapses. These synapses can have various weights (in real life due to stronger/weaker synapses from thicker/thinner connections). These weights allow a certain amount of a fired signal to pass through.
You thus have a large collection of neurons connected by synapses - the base representation for your ANN. Note that the input/output structures described by the others are an artifact of the type of problem to which ANNs are applied. Theoretically, any neuron can accept input as well. It serves little purpose in computational tasks however.
So now on to ANNs.
NEURONS: Neurons in an ANN are very similar to their biological counterpart. They are modeled either as step functions (that signal out "1" after a certain combined input signal, or "0" at all other times), or slightly more sophisticated firing sequences (arctan, sigmoid, etc) that produce a continuous output, though scaled similarly to a step. This is closer to the biological reality.
SYNAPSES: These are extremely simple in ANNs - just weights describing the connections between Neurons. Used simply to weight the neurons that are connected to the current one, but still play a crucial role: synapses are the cause of the network's output. To clarify, the training of an ANN with a set structure and neuron activation function is simply the modification of the synapse weights. That is it. No other change is made in going from a a "dumb" net to one that produces accurate results.
STRUCTURE:
There is no "correct" structure for a neural network. The structures are either
a) chosen by hand, or
b) allowed to grow as a result of learning algorithms (a la Cascade-Correlation Networks).
Assuming the hand-picked structure, these are actually chosen through careful analysis of the problem and expected solution. Too few "hidden" neurons/layers, and you structure is not complex enough to approximate a complex function. Too many, and your training time rapidly grows unwieldy. For this reason, the selection of inputs ("features") and the structure of a neural net are, IMO, 99% of the problem. The training and usage of ANNs is trivial in comparison.
To now address your GA concern, it is one of many, many efforts used to train the network by modifying the synapse weights. Why? because in the end, a neural network's output is simply an extremely high-order surface in N dimensions. ANY surface optimization technique can be use to solve the weights, and GA are one such technique. The simple backpropagation method is alikened to a dimension-reduced gradient-based optimization technique.
Simple item-to-item recommendation systems are well-known and frequently implemented. An example is the Slope One algorithm. This is fine if the user hasn't rated many items yet, but once they have, I want to offer more finely-grained recommendations. Let's take a music recommendation system as an example, since they are quite popular. If a user is viewing a piece by Mozart, a suggestion for another Mozart piece or Beethoven might be given. But if the user has made many ratings on classical music, we might be able to make a correlation between the items and see that the user dislikes vocals or certain instruments. I'm assuming this would be a two-part process, first part is to find correlations between each users' ratings, the second would be to build the recommendation matrix from these extra data. So the question is, are they any open-source implementations or papers that can be used for each of these steps?
Taste may have something useful. It's moved to the Mahout project:
http://taste.sourceforge.net/
In general, the idea is that given a user's past preferences, you want to predict what they'll select next and recommend it. You build a machine-learning model in which the inputs are what a user has picked in the past and the attributes of each pick. The output is the item(s) they'll pick. You create training data by holding back some of their choices, and using their history to predict the data you held back.
Lots of different machine learning models you can use. Decision trees are common.
One answer is that any recommender system ought to have some of the properties you describe. Initially, recommendations aren't so good and are all over the place. As it learns tastes, the recommendations will come from the area the user likes.
But, the collaborative filtering process you describe is fundamentally not trying to solve the problem you are trying to solve. It is based on user ratings, and two songs aren't rated similarly because they are similar songs -- they're rated similarly just because similar people like them.
What you really need is to define your notion of song-song similarity. Is it based on how the song sounds? the composer? Because it sounds like the notion is not based on ratings, actually. That is 80% of the problem you are trying to solve.
I think the question you are really answering is, what items are most similar to a given item? Given your item similarity, that's an easier problem than recommendation.
Mahout can help with all of these things, except song-song similarity based on its audio -- or at least provide a start and framework for your solution.
There are two techniques that I can think of:
Train a feed-forward artificial neural net using Backpropagation or one of it's successors (e.g. Resilient Propagation).
Use version space learning. This starts with the most general and the most specific hypotheses about what the user likes and narrows them down when new examples are integrated. You can use a hierarchy of terms to describe concepts.
Common characteristics of these methods are:
You need a different function for
each user. This pretty much rules
out efficient database queries when
searching for recommendations.
The function can be updated on the fly
when the user votes for an item.
The dimensions along which you classify
the input data (e.g. has vocals, beats
per minute, musical scales,
whatever) are very critical to the
quality of the classification.
Please note that these suggestions come from university courses in knowledge based systems and artificial neural nets, not from practical experience.