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Ecommerce recommender with multi events #10
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Thank you There are multiple ways of implementing a recommendation engine - once you are certain that you want to use a machine learning approach, you will need to identify features (such as the last 10 product categories viewed, etc.) that allow the estimator to learn the mapping from input to a good recommendation. This will require some ingenuity and domain knowledge on your end. Typically, learners require a fixed length feature vector as input. For this reason, if you would like to be able to consider all of the information you have regarding the user, you will need to come up with a fixed length representation of the data first (Ex. Category view counts, demographics, etc.). Again this will require some ingenuity on your part. I would start by becoming very familiar with the differences between categorical and continuous data representations. Then you have to consider what you want your model output to be. Will it be the category of a product? A specific product? A score as to how much the estimator thinks the customer will like a specific product? Once you narrow down the problem, let me know and if you had more questions we'll go from there |
Thank you @andrewdalpino |
A Probabilistic classifier that outputs the probability of purchasing every product in the catalog will do the trick. However, there are some things to consider. Is the product catalog very large? Will it change frequently? If so, this model might not scale as well as you'd expect. The prediction would look something like ... {
"guitar": 0.6,
"soap": 0.1,
"bananas": 0.3
} ... and you'd recommend the products with the highest probabilities I cannot speak too much about the feature engineering as I'm not an expert in your domain, but it sounds like you can use those events as features just fine. Again, it will require some wrangling on your part. The training label of each sample will be the product or products they've purchased as a result of those events. I am not familiar with that library either but maybe it would help to know that aside from machine learning you could also try a collaborative filtering approach |
ok, thank you Andrea. i will check it
El vie., 28 dic. 2018 a las 0:19, Andrew DalPino (<notifications@github.com>)
escribió:
… A Probabilistic classifier that outputs the probability of purchasing
every product in the catalog will do the trick. However, there are some
things to consider. Is the product catalog very large? Will it change
frequently? If so, this model might not scale as well as you'd expect.
I cannot speak about feature engineering as I'm not an expert in your
domain, but it sounds like you can use those events as features just fine.
Again, it will require some wrangling on your part.
I am not familiar with that library either but maybe it would help to know
that aside from machine learning you could also try a collaborative
filtering approach
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Andrew explained how to do it: #10
Great project!
I would like to use it for e-commerce recommendations. I have events like View Peoduct, Buy Product, Add to Cart, View Newsletter, etc.
How will you do something that consider all the information to recommend a product to a visitor based on his other actions?
Can you create some sample project like the one that predicts a House Sale Price?
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