I posted question on stat stack exchange but unfortunately got no answer so far, so I clone it here and do hope someone can help.
I'm newbie in machine learning. Recently I tried to learn something on this and got following concern:
I have products classed by categories. Also I have users with gender and device model information.
First, I made a chi square test to check whether categories and gender + device information are associated. For example, my p-value is 0.000012 so I stated that the user (gender + device) is associated with categories.
So if a new user come with his gender (Female) + device (iPhone):
As the chi square test result, there should be an association between gender + device and categories. So I select top 10 categories that were consumed by Female who using iPhone. I've got the list, e.g. [1. Fashion, 2. Mobile devices 3. Cameras, 4. Home furnitures, 5. Bikes, etc.]
I also make a z-test on categories (without any user information), and got the list (higher z-score will be on top), e.g. [1. Mobile devices, 2. Bikes, 3. Fashion, 4. Laptops, etc.]
So in this case, which list should I give to that user? Or any possibility to combine them? Or did I do something wrong?
Thanks in advance :-)
Strictly speaking, none of the tests is appropriate. In both tests you have a null hypothesis (that gender and model is not related to category), and you are trying to find the probability that this hypothesis is wrong. However, theses two tests are parametric tests, that is for the results to be correct you have to know that the probability follows a specific distribution (chi square and normal distributions respectively). In your case you can make no such assumption, so the tests are not suitable. If you want to use significance tests, you should use a non-parametric tests, Wilcoxon and Friedman tests being the most common. However, significance tests are usually used after the problem has been solved to check if the results achieved can be attributed to luck. They are not used to solve the problem.
If you want to find correlation between gender, model and category, you should use some correlation coefficient, such as Pearson correlation and intraclass correlation. However, you have not described your data in detail, so I'm not sure what you are trying to achieve. Based on gender and model only, probably the safest and simplest thing you can do is return the most visited categories (number of occurences) by women who use iPhone.
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I am trying to build a content based recommendation model but I am stuck on how to proceed with which algorithm to choose from. Basically, my features are user_id, user_age, gender, location, show_id, show_type(eg: series, movies etc.), show_duration, user_watched_duration, genre, rating. And my model has to predict top 5 recommendation shows for certain users.
The proportion of users to shows is hugh. Like there are only around 10k users but the shows that are mapped to each user is approx 150. So each user has an history of 150 shows mapped to them on an average. So total records are 10k x 150 = 15,00,000
Now, I am confused like which algorithm to proceed with this scenario. I read content based method is the ideal for my scenario. But when I checked SVD from surprise library, it is only taking 3 features as input DataSet - "user_id", "item_id", "rating" and fitting the model. But I have to consider fitting other features like user_watched_duration out of the show_duration and give preference if he/she has fully watched the show. And similarly, I want the model to consider recommending based on the Gender and age also. Like for example, for young men, (male < 20 years old) have watched a show and given a higher rating, then my recommendation to user's of similar category of young men should be given.
Can I use to train a normal classifical model like KNN for this? I tried to think of using sparse matrix using csr_matrix with row consisting of user_id and col consisting of show_id. And then transposing using (user_show_matrix.T * user_show_matrix) , so that I can use this to get counts of shows watched for that particular user. But the problem with this approach is that I cannot map other features with this, right?
So please suggest how to proceed. I already did data cleaning, label encoded categories etc. Will I be able to use any classification algorithms for this? Appreciate any references on similar approaches. Thank you!
The problem I would like to solve is how to choose the best seats on a train based on some ordered user preferences. eg. whether they'd like a seat facing forwards, backwards (or don't care), whether they'd like a seat at a table or not, whether they need to be near a toilet, luggage rack, buffet car, near the door. Window / Aisle seat. Whether they want the aisle to the left or the right (can be very important some someone with a stuff knee!).
Most customers will specify one or two preferences, other may specify more. For some, being near the toilet might be the most important factor, for others having that table to work at might be the most important.
There may be more than one passenger (although they will share preferences). These should be sat as close to each other as possible. 2 passengers would ideally be sat next to each other, or opposite each other at a table seat. A group of 8 passengers might best be split into 2 groups of 4 or 4 groups of 2...
Position is defined by carriage number (seats in the same carriage are better then seats in different carriages) and by x/y coordinate within that carriage - so easy enough to calculate distance between any pair of seats - but a BIG job to calculate distances between EVERY pair of seats...)
Each [available] seat (pre-filtered by ticket class) will have the above attributes either defined or set to NULL (for unknown - seat facing is often unknown).
So for training I can provide a vast array of example trains and customer preferences with the best balance of preferences version position.
For execution I want to provide a run-time specific array of seats with attributes, a set of user preferences and a set if weighting for those preference (eg. passenger 1 thinks being near toilet is most important, passenger 2 think having a table is most important, passenger 3 think being in the quiet carriage is..) and finally the number of passengers.
Output will be an array of seats (one per passenger) that strike the best compromise between matching as many customer preferences as possible (usually not possible to match all preferences) and keeping the seats fairly close to each other.
eg. We might be able to match 2 preferences with seats 2 rows apart, but match 3 preference with seats 10 rows apart...
Obviously distance will need a weighting the same as the individual preference and necessary to choose between those two. I suppose a distance not greater than X becomes just one more customer preference...
I've not done any ML work before, so it's all going to be a learning exercise for me. I wish I had the time to just play and see what comes out, but I don't, Happy to do that, but I need to have a reasonable expectation of a positive result otherwise I'll have to focus on a more traditional approach. Limited time and all that...
So, my questions are:
Is this a suitable problem for machine learning?
If so, is brain.js a good choice, or is something else more suitable? AWS ML service perhaps?
Any advice on how to organise all my data into something suitable for an ML engine to process?
Machine Learning is good at finding hidden patterns in complex data. In your case, you would need a lot of data where user preferences are already matched with optimal seating arrangements.
You could then try to see if the ML model can actually make optimal seating arrangements by itself. It’s an interesting problem but it may also lead to unexpected seating :)
If you don’t have training data you could collect it live, by registering where people sit down, knowing their preferences.
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
I'm a huge football(soccer) fan and interested in Machine Learning too. As a project for my ML course I'm trying to build a model that would predict the chance of winning for the home team, given the names of the home and away team.(I query my dataset and accordingly create datapoints based on previous matches between those 2 teams)
I have data for several seasons for all teams however I have the following issues that I would like some advice with.. The EPL(English Premier League) has 20teams which play each other at home and away (380 total games in a season). Thus, each season, any 2 teams play each other only twice.
I have data for the past 10+ years, resulting in 2*10=20 datapoints for the two teams. However I do not want to go past 3 years since I believe teams change quite considerably over time (ManCity, Liverpool) and this would only introduce more error into the system.
So this results in just around 6-8 data points for each pair of team. However, I do have several features(upto 20+) for each data point like Full-time goals, half time goals, passes, shots, yellows, reds, etc. for both teams so I can include features like recent form, recent home form, recent away form etc.
However the idea of just having only 6-8 datapoints to train with seems incorrect to me. Any thoughts on how I could counter this problem?(if this is a problem in the first place i.e.)
Thanks!
EDIT: FWIW, here's a link to my report which I compiled at the completion of my project. https://www.dropbox.com/s/ec4a66ytfkbsncz/report.pdf . It's not 'great' stuff but I think some of the observations I managed to elicit were pretty cool (like how my prediction worked very well for the Bundesliga because Bayern win the league all the time).
That's an interesting problem which I don't think has an unique solution. However, there are a couple of little things that I could try if I were in your position.
I share your concerning about 6-8 points per class being too little data to build a reliable model. So I would try to model the problem a bit differently. In order to have more data for each class, instead of having 20 classes I would have only two (home/away) and I would add two features, one for the team being home and other one for the away team. In that setup, you can still predict which team would win given if it is playing as home or away, and your problem has more data to produce a result.
Another idea would be to take data from other European leagues. Since now teams are a feature and not a class, it shouldn't add too much noise to your model and you could benefit from the additional data (assuming that those features are valid in another leagues)
I have some similar system - a good base for source data is football-data.co.uk.
I have used last N seasons for each league and built a model (believe me, more than 3 years is a must!). Depends on your criterial function - if criterion is best-fit or maximum profit you may build your own predicting model.
One very good thing to know is that each league is different, also bookmaker gives different home win odds on favorite in Belgium than in 5th English League, where you can find really value odds for instance.
Out of that you can compile interesting model, such as betting tips to beat bookmakers on specific matches, using your pattern and to have value bets. Or you can try to chase as much winning tips as you can, but possibly earns less (draws earn a lot of money even though less amount of draws is winning).
Hopefully I gave you some ideas, for more feel free to ask.
Don't know if this is still helpful, but features like Full-time goals, half time goals, passes, shots, yellows, reds, etc. are features that you don't have for the new match that you want to classify.
I would treat this as a classification problem (you want to classify the match in one of 3 categories: 1, X, or 2) and add more features that you can also apply to the new match. i.e: the number of missing players (due to injury/red cards), the number of wins/draws/losses each team has had in a row immediately BEFORE the match, which is the home team (already mentioned), goals scored in the last few matches home and away etc...
Having 6-8 matches is the real problem. This dataset is very small and there would be a lot of over-fitting, but if you use features like the ones I mentioned, I think you could also use older data.
It's known how collaborative filtering (CF) is used for movie, music, book recommendations. In the paper 'Collaborative Topic Modeling for Recommending Scientific 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