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Recommender Systems - PowerPoint PPT Presentation


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Sumbul Jahan. Recommender Systems. This world is an over-crowded place. They all want to get our attention. But we need a few of them!. Something which is popular. Something which is of our interest. Something which is liked by people of our community. What we are looking for?.

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Presentation Transcript
what we are looking for

Something which is popular.

Something which is of our interest.

Something which is liked by people of our community.

What we are looking for?
what is recommender system

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

what can be recommended
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

Types of recommender system

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
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.

Content Based - Limitations
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.

Collaborative Filtering

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

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.

Collaborative filtering - limitations
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
Knowledge Based
usage

Netflix

    • 2/3 rented movies are from recommendation.
Usage

http://www.shttp://pespmc1.vub.ac.be/collfilt.html

lideshare.net/T212/recommender-systems-1311490

usage1

Google News

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

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

usage2

Amazon

    • 35% sales are from recommendation.
Usage

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