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Suppose there are 2000 movies and a company wants to recommend some movies (for example, at most 5 movies) to each visitor. The objective is to learn how to predict which movie will be selected if a specific set of movies is recommended.

   option-1  option-2  option-3  option-4  option-5  Selected-Movie
1. movie1    movie3    movie4                        movie4 
2. movie3    movie4    movie100  movie1000 movie1001 movie1001
3. movie4    movie5    movie34                       movie34

Based on this data set, I want to learn when sample 1 is suggested to a customer, he will visit movie4. Because the number of features can be so high (here 2000 movies), I think it would not be a good option to use on-hot-encoding. Think at most 5 movies can be recommended, I thought it might be a good option to consider a vector with size 5 and if the number of recommended movies is less than 5, blanks will be replaced with 0. However, in this situation, the perturbation of movies will be important. For example, (1,2,3,4,5) will be different from (2,1,3,4,5) and I want to consider both cases the same. In other words, all 5! perturbations should be the same and there is no difference between them. Moreover, with this representation of data, I think it will not be possible to use Decision Tree whereas some algorithms like Catboost works.

My preference are algorithms that can generate rules like Decision Tree. I would be thankful if you have any recommendation for data representation and how features should be considered.

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In this task you're missing something: you don't have any features to represent a specific visitor.

This means that the best movie that your model can predict is the one which is selected the most often by any visitor. As a consequence, the only thing that the model can learn from such a dataset is to associate every possible sequence with the most frequently selected movie given this sequence. Of course it would have to generalize a bit for sequences which don't appear in the training data, but that's the most ML there is in this task. In theory the task can be done just by counting the joint frequency sequence+selected-movie in order to calculate the conditional probability selected-movie given sequence.


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The standard way to represent a set would be one hot encoding. It's doable even with 2000 features, assuming there are enough instances in the data. In this basic option you could certainly reduce the number of features by removing the movies which are never/rarely proposed or never/rarely selected. This would be unlikely to achieve performance, because if there are no or few cases where a movie is proposed/selected, then the model cannot use this information (or if it does it's going to cause overfitting).

I could think of a couple alternative approaches:

  • Apply clustering on the sequences of movies as OHE (without the target variable), then train a model with for every cluster: this way there are less features to deal with for every model.

  • Just rank all the movies by order of preference based on how often the movie is selected. Given that there is no user preference, it can be assumed that there is a total order on the movies:

  • the top preferred movie is always selected, whatever the other movies proposed

  • the 2nd top preferred movie is always selected, unless the first one is proposed as well

  • ... the $n^{th}$ top is always selected, unless another one higher in the top is proposed as well

To predict the selected movie, just pick the highest ranked among the 3-5 proposed.

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  • $\begingroup$ Dear @Erwan. Thanks for your response. Yes, you are right and I should add some features for each customer. It is possible to add d features representing the customers' characteristics. However, the difficult part will be the case when different movies are offered here. In my problem, the probability that a customer chooses a movie depends on both number of movies and each individual movie. For example, if a movie with rate 7 is offered with another with rate 4, it is more probable that the first movie will be selected. $\endgroup$ – Katatonia Jan 10 at 3:40
  • $\begingroup$ However, if it is offered with a movie with rate 9.6, the second one might be more probable. Actually, if I consider just the current format of features, the learning algorithm should map a set to a real number which is different from former cases in which a vector is mapped to a real number. Here, I don't know how to remove the order of movies in features. $\endgroup$ – Katatonia Jan 10 at 3:40
  • $\begingroup$ @Katatonia do you mean that you have the rating of every movie as feature? And do you want to assume that the most likely selected movie is always the one which has the highest rating? If so there's no ML at all, you can just pick the max rating. $\endgroup$ – Erwan Jan 10 at 10:52
  • $\begingroup$ No, ratings are not features, just movies are features. For example (movie1, movie2, movie3) is a sample and the output can be movie3. It is not necessary that the most likely movie is the movie with the highest rating. For example, if a high rate movie with an irrelavant genre is offered, it is not likely to be selected movie. My main question is how to represents features (movies) such that orders are not important. $\endgroup$ – Katatonia Jan 10 at 19:17
  • $\begingroup$ @Katatonia I updated my answer with several options. $\endgroup$ – Erwan Jan 10 at 23:51

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