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In google's prediction api page, it says we can use it for recommendation of webpages / products...

Can someone please show me how, for example:

I have 500,000 members purchased history

I have 2,000,000 products in 200 different categories

I have user-X just signup, asked him 15 'like' / 'dislike' product questions (user's taste)

Now, i want to suggest/recommend user-X with a list(e.g. 500) of products which he most likely willing to purchase

Thanks a lot

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2 回答 2

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The Google Prediction API, as currently implemented, is great for classifying data into a discrete set of categories, however, as noted in the documentation:

Avoid having a high ratio of categories to training data in categorical models. Try to have at least a few dozen examples for each category, minimum. For really good predictions, a few hundred examples per category is recommended.

The Prediction API's classification doesn't work well when the ratio of categories to examples is high and in the example you sketched the relationship is one-to-one because you are trying to find the user whose liked product list is most similar to the user of interest (to find a set of promising products to recommend). In this model, each user is a unique category.

于 2012-11-15T06:37:07.803 回答
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If you are not specifically tied to Google API fow whatever reason, explore using Mahout. This is a basic use case for the Mahout Recommendation mining.

https://cwiki.apache.org/MAHOUT/itembased-collaborative-filtering.html

于 2012-11-23T06:49:06.687 回答