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User Modeling and Recommender
Systems: Introduction to recommender systems
Adolfo Ruiz Calleja06/09/2014
Index
2
• What is a recommender system?• Classification of recommender systems• Introduction to the main paradigms of
recommender systems• Example: Amazon
Index
3
• What is a recommender system?– Approacher to avoid information overload– Definition of Recommender Systems– Some examples– Added value of the Recommender Systems
• Classification of recommender systems• Introduction to the main paradigms of
recommender systems• Example: Amazon
Approaches to avoid information overload
4
• Information retrieval (IR)– Static content + dynamic query– The content is modelled– Example: a library search system
• Information filtering (IF)– Static query + dynamic content– The query is modelled– Example: anti-spam filter
Definition of Recommender Systems
5
Recommender Systems (RS) are information filtering systems that seek to predict the preference that a user would give to an item
USER ITEM
Algorithm
rating
Set of user attributes
Set of user attributesSet of user
attributesSet of user attributesSet of user
attributesSet of user attributes
Set of user attributesSet of user
attributesSet of user attributesSet of user
attributes
Some Examples
6
Some Examples
7
Some Examples
8
Some Examples
9
Added value of the Recommender Systems
10
• Provision of personalized recommendations– But it requires that the maintain a user profile
• Allows to persuade each customer with personalized information
• Serendipitous discovery• Enables to deal with the long tail– Which is very important in the Web
Added value of the Recommender Systems
11
Index
12
• What is a recommender system?• Classification of recommender systems– Different classifications– Domain of the recommendation– Purpose of the recommendation– Context of the recommendation– Data collected– Recommendation algorithm
• Introduction to the main paradigms of recommender systems
• Example: Amazon
Different classifications
13
• Domain of the recommender system• Purpose of the recommendation• Context of the recommendation• Data collected• Recommendation algorithms• Others
• Privacy• Interfaces• Software architecture
Domain of the recommendation: What is
being recommended?
14
• Many different examples– Text documents (web pages, news…)– Media (music, movies…)– Products (or product bundles)– Vendors– People– Sequences
• Huge impact on the recommendation algorithm– Should it recommend twice the same item?– How important is time?
Purpose of the recommendation
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• The recommendation itself– E.g. sale a product
• Education of the users– E.g. track user behavior to provide recommendations
• Build a community around a particular product– E.g. booking
Context of the recommendation: What is the user doing?
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• Can the user be interrupted? – E.g. listening to music vs. shopping
• Is the user alone or within a group?– E.g. recommend items to users vs. to groups
Data collected
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• How are the recommended items described?• How are they collected?• Whose opinion does the algorithm collect? • How is this opinions collected?• How are the profiles created?– Explicit / Implicit
• What kind of personal information is collected?– It opens several ethical issues
Recommendation algorithm
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• Which information is taken into account to make the recommendation?
• How honest is the recommendation?– Business rules may affect– External manipulation
• Transparency of the algorithm
Index
19
• What is a recommender system?• Classification of recommender systems• Introduction to the main paradigms of
recommender systems– Idea– Not personalized– Content-based recommendation– Knowledge-based recommendation– Collaborative recommendation
• Example: Amazon
Idea
20
USER ITEM
Algorithm
rating
Set of user attributes
Set of user attributesSet of user
attributesSet of user attributesSet of user
attributesSet of user attributes
Set of user attributesSet of user
attributesSet of user attributesSet of user
attributes
Not personalized
21
• Based on External Community Data• Very little information from the user (if any)• Simple algorithms• They forget about the long tails
• Example: Tripadvisor or Billboard
Content-based recommendation
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• User model is built analyzing user preferences and item attributes
• Very little information from the user (if any)• Do not need to count with a large group of users• It is hard for them to deal with subjective
characteristics of items
• Hard to found massively used examples– Personalized news feeds
Knowledge-based recommendation
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• Subclass of content-based recommender systems• Need explicit information “from the outside”– Included by the user (constraint-based)– Knowledge from experts in the domain (cased-based)
• Can deal with time spans• Can deal with visitors that only appear once
• House, car or technology recommendation– Realtor
Collaborative recommendation
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• Item model is a set of ratings• User model is a set of ratings• Many different techniques to match the ratings• What to do with new things/people/systems?
• Predominant paradigm
Index
25
• What is a recommender system?• Classification of recommender systems• Introduction to the main paradigms of
recommender systems• Example: Amazon