The document discusses recommender systems which help users find items that meet their needs. It describes three common recommendation approaches: collaborative filtering, content-based filtering, and hybrid recommender systems. Collaborative filtering makes predictions about a user's interests based on preferences of many users, while content-based filtering relies on descriptions of items and a user's preference profile. The document also outlines the speaker's research into a hybrid food recommender system that recommends restaurants and foods based on a user's taste preferences and location.
5. why do we need a recommender
engine?
• Increase the number of items sold
• Sell more diverse items
• Increase the user satisfaction
• Increase user fidelity
• Better understand what the user
wants
10. collaborative filtering
a method of making automatic predictions (filtering)
about the interests of a user by collecting
preferences or taste information from many users
(collaborating)
11. USER & ITEM
http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
12. 13
ORDER DATA
http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
13. 14
ORDER DATA (cont.)
http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system
14. 15
ORDER DATA (cont.)
http://www.slideshare.net/lonelywolf/how-to-build-a-recommender-system