CODEWITHSUNDEEP
servicerecommendation-enginecollaborative-filtering
Katka
Katka

Reputation: 324

Item recommendation service

I'm supposed to make book recommendation service using MyMediaLite. So far I have collected books from website using Nutch crawler and storing info into hbase. The problems is that I actually not fully understand, how all this thing works. By examples, I have to pass a test data and training data files, with user-item id pairs and rating. But what about other information of book, like categories and authors? How it is possible to find "similar" books, by their information etc, without information about user (so far)? Is it possible to pass data directly from hbase, without storing it to file and then leading in? Or for this job better suits Apache Mahout or LibRec?

Upvotes: 0

Views: 189

Answers (1)

Dan Jarratt
Dan Jarratt

Reputation: 380

User-item-rating information, often in a matrix, is the basis for collaborative filtering algorithms (user-user CF, item-item CF, matrix factorization, and others). You're using other people's opinions to form recommendations. There's no innate recognition of the content of the items themselves. For that, you'll need some sort of content-based filtering algorithm or data mining technique. These are often used in the "user cold start" scenario you described: you have lots of information about items but not about a particular user's preferences.

First, think about your end goal and the data you have. Based on your product needs and available data, you can choose the right algorithm for your purposes. I highly recommend the RecSys course on Coursera for learning more: https://www.coursera.org/learn/recommender-systems. It's taught by a leader in the field.

Upvotes: 0

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