The document discusses recommender systems, explaining their purpose in reducing information overload and enhancing user experience through various types such as collaborative, content-based, and hybrid systems. It outlines the value of these systems for both customers and providers, highlighting their effectiveness in increasing sales and user engagement. Additionally, it addresses the evaluation techniques used to measure their success and provides a brief overview of developing a book recommender system.

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Book Recommended
Systems

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What is Recommended Systems

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© Dietmar Jannach, Markus Zanker and Gerhard Friedrich

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© Dietmar Jannach, Markus Zanker and Gerhard Friedrich
Recommender Systems
Application areas

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© Dietmar Jannach, Markus Zanker and Gerhard Friedrich
In the Social Web

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What are recommender systems for?
l Introduction
How do they work (Part I) ?
l Collaborative Filtering
How to measure their success?
l Evaluation techniques
How do they work (Part II) ?
l Content-based Filtering
l Knowledge-Based Recommendations
l Hybridization Strategies
Advanced topics
l Explanations
l Human decision making
Agenda

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Introduction

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Why using Recommender Systems?
Value for the customer
l Find things that are interesting
l Narrow down the set of choices
l Help me explore the space of options
l Discover new things
l Entertainment
l …
Value for the provider
l Additional and probably unique personalized service for
the customer
l Increase trust and customer loyalty
l Increase sales, click trough rates, conversion etc.
l Opportunities for promotion
l Obtain more knowledge about customers
l …

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Real-world check
Myths from industry
l Amazon.com generates 30-40 percent of their sales through the recommendation
lists
l Netflix (DVD rental and movie streaming) generates 20-25 percent of their sales
through the recommendation lists
There must be some value in it
l See recommendation of groups, jobs or people on LinkedIn
l Friend recommendation and ad personalization on Facebook
l Song recommendation at last.fm
l News recommendation at Forbes.com (plus 37% CTR)

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Recommender systems
Recommender systems reduce information overload by estimating relevance

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Types of Recommender Systems?
l
Personal recommender systems
l
Collaborative recommender systems
l
Content-based recommender systems
l
Knowledge-based recommender systems
l
Hybrid recommender systems

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Paradigms of recommender systems

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Collaborative recommender systems
Collaborative: "Tell me what's popular
among my peers"

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Content-based recommender systems
Content-based: "Show me more of the
same what I've liked"

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Knowledge-based recommender systems
Knowledge-based: "Tell me what fits
based on my needs"

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Hybrid recommender systems
Hybrid: combinations of various inputs
and/or composition of different
mechanism

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Evaluation of Recommender Systems

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What is a good recommendation?
Total sales numbers
Promotion of certain items
…
Click-through-rates
Interactivity on platform
…
Customer return rates
Customer satisfaction and loyalty
What are the measures in practice?

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Group B : Assignments No. 7
l
Developing an book recommend-er ( a book that the
reader should read and is new) Expert system
l
Flow of application :
l
Top Rated and New added book will
recormanded first.
l
Functionality provide for add new book
l
Database: SQLite
l
Control Used :

Listview: To show list on screen

Spinner: To show drop down on
screen

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Screens
l
Top rated books
Recommender
by system

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Screens
l
Click on Book &
make
Recommendation
i.e give rating

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Screens
l
Add New book.

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Thank you for your attention!
http://recsys.acm.org
http://www.recommenderbook.net