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I am working on a supervised learning problem for a web-search task, where I have access to a relatively small set of human-labeled examples and lots of user-behavior data.

Now, user behavior data is biased, because of presentation bias, position bias etc. So it's likely that its' distribution will be different from human-labeled data.

I am planning to use both to train a Neural Network model.

Now I am confused about how to combine both datasets?

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  • $\begingroup$ nice topic, thanks for asking about it; I am not sure about understanding the bias due to the user behaviour, can you give any more detail on it? $\endgroup$
    – German C M
    Aug 11, 2020 at 7:18
  • $\begingroup$ Thanks for responding. So the task is query classification into some predefined categories. From user clicked documents, can create a dataset, but it will be biased by documents on the top position, plus the mode of presentation etc. $\endgroup$ Aug 11, 2020 at 13:09
  • $\begingroup$ Could you provide some samples and/or the structure of your data $\endgroup$ Aug 17, 2020 at 13:51
  • $\begingroup$ I think you are looking for semi-supervised methods. $\endgroup$ Aug 17, 2020 at 16:32
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    $\begingroup$ @EngrStudent I am not sure if it falls under the semi-supervised umbrella. We have two kinds of labelled data. One of them is human-labelled, the other one is derived from the click-log. In semi-supervised learning, you have lots of unlabelled data. $\endgroup$ Aug 18, 2020 at 6:49

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That is a common scenario in a learning to rank problem. One heuristic is to separately model explicit (human-labeled) and implicit (user-behavior) features. Then combine the separate feature groups with a learned weight for their final relative contribution. Improving Web Search Ranking by Incorporating User Behavior Information by Agichtein et al goes into greater detail.

RankNet takes this approach using a neural network.

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