I have a data set with 100,000 instances and about 40 features. Each instance is a customer and each feature is a property of the customer. The first column is binary 0/1 which indices whether the customer click the ads. The task is fitting the data with models or one model and predicting if a new customer will click the ads or not.

I want to start with only one kind of model to do this but don't know which model is suitable. I can think svm (with libsvm), logistic regression. I don't think matrix factorization methods can help because the columns are features of the customer, not items.

  • $\begingroup$ Can you provider more informations about the items in this recommendation system? Do you have only one Ads or multiples? $\endgroup$
    – Anis H
    Nov 20, 2015 at 6:07
  • $\begingroup$ @imanis_tn Just only one ads. And I agree with Adrien Renaud. May be I need a classifier, not a recommender system. $\endgroup$ Nov 20, 2015 at 8:19

1 Answer 1


Given the nature of the data shown in your question, it seems that your are looking for a classifier algorithm and not a recommender system.

A recommender system would answer the question: "What particular ads to show to this particular user". Your question is really different: "Will a new customer click the ads or not".

To build a recommender system, you need to record interactions between users and items. In your case, it seems that you have only one item. Intuitively it seems clear that you can't build a recommender with one item.

With only one item, you are good to use classifier algorithm. Choosing a model for a binary classification problem is a very broad subject. You mentioned two very good examples, svm and logistic regression. I would add a third one that is very often used: boosted decision trees like in adaboost or xgboost.

  • 1
    $\begingroup$ ...beyond classification some kind of probability estimate of the customers clicking the add or not may be useful to optimize the gains of a given strategy. That should be attainable by all recommended models. $\endgroup$ Nov 19, 2015 at 21:13

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