skotz/cp-user-behavior
Recommendation engine using collaborative filtering and matrix factorization
This tool helps you suggest items to your users based on their past actions and what similar users have liked. You provide data on how users have interacted with items (like articles or products), and it generates a list of personalized recommendations. This is ideal for product managers, content curators, or e-commerce professionals looking to enhance user experience and engagement.
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Use this if you need to offer personalized suggestions to your users based on their historical behavior and patterns observed from a large user base.
Not ideal if you don't have existing user interaction data, or if your recommendations need to be based on factors other than collaborative behavior, such as item attributes or real-time context.
Stars
33
Forks
12
Language
C#
License
MIT
Category
Last pushed
Nov 06, 2018
Commits (30d)
0
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