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I'm trying to create a content based recommender system. The system accuracy is quite enough when finding similar items but it's not as good as when recommending items to a specific user.

I use categories, publisher and manufacturers to find similarities between books. I've converted these features to vectors. With the help of cosine similarity, I've found similarity scores between books.

I thought that, if I want to recommend books to user according to its taste, I should convert user to a vector that is combination vector of books that user has liked or bought. The problem occurred here. If one of book that user has liked/bought have more features than the other books, then all recommended books are similar to more featured book. I don't know how to solve this problem.

Is my way to recommend personalized products correct? If it's what can I do to avoid this problem?

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I've solved the problem with giving weights to products. Products with more features have less weight (Divide vectors to number of features).

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    Oct 8, 2022 at 15:50

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