RECOMMENDER SYSTEMSSumbul Jahan

THIS WORLD IS AN OVER-CROWDED PLACE

THEY ALL WANT TO GET OUR ATTENTION

BUT WE NEED A FEW OF THEM!

WHAT WE ARE LOOKING FOR? Something which is popular. Something which is of our interest. Something which is liked by people of

our community.

WHO CAN HELP???

RECOMMENDER SYSTEMS

WHAT IS RECOMMENDER SYSTEMAn information filtering technology, commonly used on e-commerce Web sites that uses a collaborative filtering to present information on items and products that are likely to be of interest to the reader.

http://citationmachine.net/index2.php?reqstyleid=2

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

TYPES OF RECOMMENDER SYSTEM Content Based

Collaborative Filtering

Knowledge Based

http://www.umiacs.umd.edu/~jimmylin/INFM700-2008 Spring/presentations/recommender_systems.ppt.

CONTENT BASED 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 - LIMITATIONS 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.

COLLABORATIVE FILTERING 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.

http://pehttp://pespmc1.vub.ac.be/collfilt.html

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

COLLABORATIVE FILTERING - LIMITATIONS 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.

KNOWLEDGE BASED 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

USAGE Netflix

2/3 rented movies are from recommendation.

http://www.shttp://pespmc1.vub.ac.be/collfilt.htmllideshare.net/T212/recommender-systems-1311490

USAGE Google News

More than 38% click-through are due to recommendation.

http://www.slideshare.net/T212/recommender-systems-1311490

USAGE Amazon

35% sales are from recommendation.

http://www.slideshare.net/T212/recommender-systems-1311490