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Recommender Systems

In a world overflowing with information, how do we find what truly matters to us? Enter recommender systems, the information filtering technology used on e-commerce websites to tailor suggestions based on our interests. This comprehensive guide explores the various types of recommender systems, from content-based to collaborative filtering, offering insights on how they work, their limitations, and real-world applications. Whether it's recommending music tracks, movies, restaurants, or even future friends, recommender systems play a crucial role in shaping our choices and experiences in a crowded digital landscape.

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Recommender Systems

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  1. SumbulJahan Recommender Systems

  2. This world is an over-crowded place

  3. They all want to get our attention

  4. But we need a few of them!

  5. Something which is popular. Something which is of our interest. Something which is liked by people of our community. What we are looking for?

  6. WHO CAN HELP???

  7. Recommender systems

  8. An information filtering technology, commonly used on e-commerceWeb sites that uses a collaborative filtering to present information on items and products that are likely to be of interest to the reader. What is recommender system http://citationmachine.net/index2.php?reqstyleid=2

  9. What can be recommended • Advertising messages • Investment choice • Restaurants • Cafes • Music tracks • Movies • TV programs • Books • Stores • Tags • News articles • Future friends • Research papers • Citations • Courses • Articles • Supermarket goods • Products/Services http://www.slideshare.net/T212/recommender-systems-1311490

  10. Content Based Collaborative Filtering Knowledge Based Types of recommender system http://www.umiacs.umd.edu/~jimmylin/INFM700-2008 Spring/presentations/recommender_systems.ppt.

  11. Recommend items based on user’s past preferences. Items/content usually denoted by keywords. Matching “user preferences” with “item characteristics” … works for textual information. User profile is the key. Content Based

  12. Not all content is well represented by keywords, e.g. images. No profile is available for new users. Unrated items are not shown. Users with thousands of purchases is a problem. Content Based - Limitations

  13. Recommend items based on ratings of users sharing similar interests. Collaborative filtering systems can produce personal recommendations by computing the similarity between your preference and the one of other people. More users, more ratings: better results. Collaborative Filtering http://pehttp://pespmc1.vub.ac.be/collfilt.html

  14. http://www.bridgewell.com/images_en/ec_03.jpg

  15. Different users might use different scales. Possible solution: weighted ratings, i.e. deviations from average rating. Finding similar users/user group is not very easy. No preference is available of new users. No rating is available of new items. Collaborative filtering - limitations

  16. Knowledge of user is linked to knowledge of products. • Conversational interaction used to establish current user preferences i.e. “more like this”, “less like that”, “none of those” … • No user profiles maintained, preferences drawn through manual interaction Knowledge Based

  17. Netflix • 2/3 rented movies are from recommendation. Usage http://www.shttp://pespmc1.vub.ac.be/collfilt.html lideshare.net/T212/recommender-systems-1311490

  18. Google News • More than 38% click-through are due to recommendation. Usage http://www.slideshare.net/T212/recommender-systems-1311490

  19. Amazon • 35% sales are from recommendation. Usage http://www.slideshare.net/T212/recommender-systems-1311490

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