O'Reilly logo

Building Recommendation Engines by Suresh Kumar Gorakala

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Model-based recommender systems

Till now we have been focusing on neighborhood approaches which involve similarity calculations between users or products for collaborative filtering approaches or represent the user and item contents in a vector space model, and find similarity measures to identify items similar to the preferences of the users. The main objective of the similarity-based approaches is to calculate the weights of the preferences of users for the products or product content and then use these feature weights for recommending items.

These approaches have been very successful over the years and even today. But these approaches have their own limitations. Since entire data has to be loaded into the environment for similarity calculations, ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required