Overview of collaborative filtering algorithms

Content

Section 0. Introduction

Section 1 : User-based method

Section 2. Item-based method

Section 3. Model-based method

Section 4. Summary

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Section 0 : Introduction

The motivation for collaborative filtering comes from the idea that people often get the best recommendations from someone with tastes similar to themselves. Collaborative filtering encompasses techniques for matching people with similar interests and making recommendations on this basis.

Generally speaking, there are three types of collaborative filtering recommendations.

• The user-based method
• The item-based method
• The model-based method

Section 1 : User-based method

The User-based method mainly considers the similarity between users and users. By finding out the items that similar users like and predicting the target users’ ratings of the corresponding items, we can find a number of items with the highest ratings and recommend them to users.