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When a user searches for an item to purchase on a retail website they can input some features of their desired item to narrow down their search results. This produces a list of items that match their search. They can subsequently click on and 'view' some of these items that tickle their fancy.

However, I only have data for their search parameters (the features and their values) and also the items they view afterwards. I do not have the list of items from the search. Given this, how would I go about evaluating a machine learning algorithm that tries to learn what items a user views, given this lack of data?

E.g. What would you separate into training/test data? Would the domain space of items be all possible items on the retail website which leads to a huge imbalance in classes?

I have thought about generating synthetic data from the search parameters or sampling across different users, however both methods seem ugly and unsound.

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  • $\begingroup$ This question is difficult to understand. Please consider breaking it into shorter questions or summarize it into one question. $\endgroup$ – Kinformationist Aug 15 '17 at 17:46
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I think you may be able to develop a predictive model with the search parameters as your independent variables and view items as your target variable. I would not choose all items on the website as domain. Instead just choose your domain to be the searched queries and the resulting viewed items as your domain. Divide this domain into a typical 75% vs 25% train and test and then check the performance of your ML algorithm.

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