E.g. I want to measure how flexible customers for different brands of goods by looking at their searching behaviors in the past months.
Users could have below searching logs
User 1: Brand A
User 2: Brand A A A A
User 3: Brand A A B A
User 4: Brand A A B B
So it is clear that user 1 and 2 have less flexibility than 3 and 4 and they tend to buy only from one brand.
While user 2 is less flexible than user 1 since 2 have multiple searches to show its inflexibility while 1 we don’t have that same amount of confidence.
So intuitively the degree of flexibility are 2<1<3<4
Initial I want to use entropy to calculate but it can’t distinguish user 1 and 2 as entropy will be both 0.
Do you know any method to calculate the flexibility of each user using one numeric value that also take the frequency of each unique value into account? Really appreciate
Related
I am running a survey on REDCap where participants need to be assigned to one of three groups before receiving a group-specific intervention to reduce their smartphone use (e.g., 1 - intervention one, 2 - intervention 2, 3 - intervention 3). I have tried using the randomization module, but it requires one to allocate each record manually. For this specific study it becomes a problem, because we want to collect data from hundreds of people who will be completing the study all over the world, meaning that I cannot be on the computer at all times manually randomizing people and entering their records.
Is there a way to set up the randomization (or any other method) so that participants are randomly assigned to one of the three groups?
As you noted, randomisation in REDCap must be executed by a user with sufficient rights to do so, and ordinarily cannot be automated. But there are other options.
Realtime Randomization
You should reach out to your local REDCap administrators, as they may be amenable to installing the Realtime Randomization External Module, which may provide you the functionality that you want. This will (I think) automate the execution of the randomize button when a form is completed. Whether it works on surveys I don't fully know. Assuming it does, this is advantageous as it will uses the pre-defined randomisation allocation table that you generate outside REDCap, possibly with the help of a statistician. This is preferred if you need real randomisation.
Pseudo-randomisation
If you don't need to use a pre-defined randomisation allocation table, and can get by with each successive participant being allocated to a different group (record 1 -> intervention 1, record 2 -> intervention 2, record 3 -> intervention 3, record 4 -> intervention 1, etc.), so in fact not random at all, but sort of gated, then you can use the record ID in a calculated field to determine which of the three interventions a record should be allocated to. To do this you should return the modulo of the record ID by 3:
[record-name] - (rounddown([record-name]/3) * 3)
This will return 1, 2 and 0 for record IDs 1, 2 and 3, respectively, and for 4, 5 and 6, respectively, and so on ad infinitum.
Then, from this value, you can use standard branching logic to display a different fields, direct respondents to different surveys using logic in the survey queue, invite them to specific instruments using logic in automated survey invitations, fire off different alerts with instructions for each intervention group, etc.
I'd like to test CB for e-commerce task: personal offer recommendations (like "last chance to buy", "similar positions", "consumers recommend", "bestsellers", etc.). My task is to order them (more relevant issue is higher in the list of recommendations).
So, there are 5 possible offers.
I have some historical data collected without using any model: context (user and web-session features), action id (one of my 5 offers), reward (1 if user clicked this offer, 0 - not clicked). So I have N users and 5 offers with known reward, totally 5*N rows in my historical data.
Ex:
1:1:1 | user_id:1 f1:... f2:...
2:-1:1 | user_id:1 f1:... f2:...
3:-1:1 | user_id:1 f1:... f2:...
This means that user 1 have seen 3 offers (1,2,3), cost of the 1 offer is equal to 1 (didn't click), user ckickes on offers 2 and 3 (cost is negative -> reward is positive). Probabilities are equal to 1, since all offers were shown and we know rewards.
Global task is to increase CTR. I'd like to use this data for training CB and then improve the model by exploration/exploitation policies. I set probabilities equal to 1 in this data (Is it right?). But next I'd like to set the order of offers according to rewards.
Should I use for this warm start in VW CB? Will this work correctly with data collected without using CB? Maybe you can advise more relevant methods in CB for this data and task?
Thanks a lot.
If there are only 5 possible offers and if you (as indicated) have data of the form "I have N users and 5 offers with known reward, totally 5*N rows in my historical data." then your historical data is supervised multilabel data and the warm-start functionality would apply; make sure you use the cost-sensitive version to accommodate the multilabel aspect of your historical data (i.e., there is more than one offer that would result in a click).
Will this work correctly with data collected without using CB?
Because the every action-reward is specified for every user in the data set, you only have to ensure that the sample of users is representative of the population you care about.
Maybe you can advise more relevant methods in CB for this data and task?
The first paragraph started with "if" because the more typical case is 1) there are many possible offers and 2) users have only seen a few of them historically.
In such case what you have is a combination of a degenerate logging policy and multiple rewards being revealed. If there are k possible actions but each user has only seen n<=k historically then you could try and make n lines for each user as you did. Theoretically this does not necessarily work but in practice it might help.
Out of the box: change the data
If the data you have was collected as the result of running an existing policy, then an alternative would be to start randomizing the decisions made by that system in order to collect a dataset which conforms to CB. For example, use your current system to pick the "best" action 96% of the time, and one of the other 4 actions at random 4% of the time, and log the probability along with the reward (either 0.96 or 0.01 depending upon whether it was the considered best), and then set up a proper CB-style training set for vw. With this you can also counterfactually estimate the value of both your current policy and the policy vw generates, and only switch to vw when it is winning.
The fastest way to implement the last paragraph is to just start using APS.
I have to split a group of x people into 3 or 4 groups, most likely 3.
I want people to be happy, so I'm having each person rate the other members of the big group from 1 to (x-1).
How do I optimize preferences to create 3 groups?
Here is a method that is likely to get a good arrangement, even if it is not an optimal arrangement:
First create a ranking function that can take any pair of groupings and determine whether one is better than the other. Then apply the following algorithm:
Randomly assign people into groups.
Randomly pick one person from each group.
Create new groupings in which each combination of reassignments is performed on the people chosen in step 2. (For 3 groups there will be 6 such reassignments. For 4, 24.)
Of all possible reasignments, pick the best one.
Repeat steps 2–4 one million times.
UPDATE
If there are only 18 people that need to be assigned, then that's just (18 choose 6) * (12 choose 6) / 6 = 2,858,856 possible groupings. (Or, in the case of four groups it's (18 choose 4) * (14 choose 4) * (10 choose 5) / 4 = 192,972,780 groupings.)
You can just try each one and pick the best.
I guess the ranking algorithm itself is really the hard part of this assignment.
You could just give each person a score based on summing the scores of the people selected to be in their group, then sum the scores of each person together.
The problem is that you're going to end up with all the popular people in one group, and all the unpopular people in another group, and all the telephone handset cleaners in another group.
You should just assign people randomly, and then tell them that you used some really scientific system. That way everybody gets a good mix.
Measure the total satisfaction of a given configuration by calculating the distance between the actual positions and the stated preferences. Start with a randomized set of groups. Then use something like hill climbing or simulated annealing to optimise.
http://en.wikipedia.org/wiki/Hill_climbing
http://en.wikipedia.org/wiki/Simulated_annealing
Simulated annealing sounds complicated, but it's really just a cleverer version of hill-climbing.
I am talking about something like movie/item recommendation, but it seems that real estate is more tricky. When visiting a web-site and doing some search for RE, the user should be presented with some suggestions. Let's separate the task in two tasks:
a) the user has still not entered any personal info - item based recommendation
b) the user has already entered his/hers details such as income, location, etc. - item/user based recommendation
The first thing that comes to my mind for task a) is to start modeling RE features, but using some ranges instead of exact values. For example:
Area in m2
40 - 50 we can mark it for "1"
50 - 70 is "2"
etc ...
Price:
20 - 30 thousands € will be marked as 1
30 - 40 will be 2
etc ...
Proximity to city center:
1 for the RE being within the city center
2 for Zone 2 or up to 2/3 kilometers from center
3 for Zone 3 or 7 kilometers from center
So having ranges lets us assign a vector to each RE property which will allows us to use: Euclidean distance, Pearson correlation and some nearest neighbor algorithms.
Please comment on my approach or suggest a new one.
If you already have a website with enough traffic, you can try a pure collaborative filtering approach, i.e people who viewed this property also viewed these other properties. You could use the Pearson correlation there for good results.
Similarity between 2 RE can be defined as
number of people who viewed both RE1 and RE2
sim = ---------------------------------------------
number of people who viewed either 1 or both
When a user is viewing property RE you can sort all other RE properties based on the similarity score with the property being shown and show the top few.
You could add some obvious filters on top of this like the location of the property, the price range etc.
You can also define the similarity as you have suggested and mix the results from both for good representation from new RE entries which do not have a high chance of getting in if a pure collaborative filtering algorithm is used.
I'm creating a site whereby people can rate an object of their choice by allotting a star rating (say 5 star rating). Objects are arranged in a series of tags and categories eg. electronics>graphics cards>pci express>... or maintenance>contractor>plumber.
If another user searches for a specific category or tag, the hits must return the highest "rated" object in that category. However, the system would be flawed if 1 person only votes 5 stars for an object whilst 1000 users vote an average of 4.5 stars for another object. Obviously, logic dictates that credibility would be given to the 1000 user rated object as opposed to the object that is evaluated by 1 user even though it has a "lower" score.
Conversely, it's reliable to trust an object with 500 user rating with score of 4.8 than it is to trust an object with 1000 user ratings of 4.5 for example.
What algorithm can achieve this weighting?
A great answer to this question is here:
http://www.evanmiller.org/how-not-to-sort-by-average-rating.html
You can use the Bayesian average when sorting by recommendation.
I'd be tempted to have a cutoff (say, fifty votes though this is obviously traffic dependent) before which you consider the item as unranked. That would significantly reduce the motivation for spam/idiot rankings (especially if each vote is tied to a user account), and also gets you a simple, quick to implement, and reasonably reliable system.
simboid_function(value) = 1/(1+e^(-value));
rating = simboid_function(number_of_voters) + simboid_function(average_rating);