Recommendtion System Introduction

collaborative filtering

producing recommendations based on, and only based on, knowledge of users’ rela-tionships to items. These techniques require no knowledge of the properties of the items themselves.

In fact, these are the two broadest categories of recommender engine algorithms: user-based and item-based recommenders.

 

 

content-based recommendation

based on the attributes of items. There’s nothing wrong with content-based techniques; on the contrary, they can work quite well. They’re necessarily domain-specific approaches, and they’d be hard to meaningfully codify into a framework. To build an effective content-based book rec-ommender, one would have to decide which attributes of a book—page count, author,publisher, color, font—are meaningful, and to what degree. None of this knowledgetranslates into any other domain; recommending books this way doesn’t help in the area of recommending pizza toppings.

 

 

 

 

 

 

 

Reference

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