How to get k similar products using Mahout?

I'm going to guess at the question here. You have user-item data, where users are real people and items are books. You are using LogLikelihoodSimilarity as the basis for some recommender, either user-based or item-based.

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I have one product, let's say a book. Now I want to retrieve k products, that are similar to this product. How can I do this with Mahout?

The products are stored in a MySQL database so I'd use the JDBCDataModel. For computing the similarities I'd prefer the LogLikelihoodTest. But which recommender should I choose?

It seems that all recommenders are designed mahout link|improve this question asked Jan 9 at 11:09brainfck1,0351617 91% accept rate.

Designed...? Finish the thought and I'm pretty sure I can answer. – Sean Owen Jan 9 at 18:38 Sorry, it was saying that "all recommenders are designed to be used with user data". – brainfck Jan 9 at 22:42.

I'm going to guess at the question here. You have user-item data, where users are real people and items are books. You are using LogLikelihoodSimilarity as the basis for some recommender, either user-based or item-based.

You don't need a recommender if you just want most similar items. Just use LogLikelihoodSimilarity, which is an ItemSimilarity, to compute similarity with all other items and take the most similar ones. In fact look at the TopItems class which even does that logic for you.

Thanks, so I gonna stick with the TopItems class! Thanks! :) – brainfck Jan 9 at 22:42 BTW, thanks for your great book!

– brainfck Jan 10 at 9:46 NP and I forgot to mention an even easier option... use an ItemBasedRecommender and call mostSimilarItems() which does the above for you. – Sean Owen Jan 10 at 9:53 That's the recommender I'm looking for! Awesome!

:) – brainfck Jan 10 at 16:22.

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