Building Recommender for book paragraphs I have some application which are offering a book to read. Users normally read some paragraphs of it only (it contains +6000 paragraphs).
Looking at scatter for users vs paragraphs:

Which you can see is semi equal distributed. Using SVD algorithm for matrix factoize gives a semi random predictions. I have total of 18k records of user read paragraphs. Looking for users, it seems that a user is reading semi random set (i.e. it is hard to specify common topics for a single user readings)
Can you suggest me how to produce suggestions related to each user ?
You would have to come up with a metadata set of each paragraph and then create a content-based recommendation system based on that metadata. You could also do a community-based recommendation system if you have extensive user data. 

As for the metadata set, you could use something like n-grams to "summarize" each paragraph and then have an index for each. From there, you would know what people like and then algorithmically choose the n-grams that person is most interested in and find the paragraphs that correspond to those n-grams.