User Modeling and Recommender

Systems: Introduction to recommender systems

Adolfo Ruiz Calleja06/09/2014

Index

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• What is a recommender system?• Classification of recommender systems• Introduction to the main paradigms of

recommender systems• Example: Amazon

Index

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• 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

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• 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

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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

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Some Examples

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Some Examples

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Some Examples

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Added value of the Recommender Systems

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• 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

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Index

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• 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

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• 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?

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• 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

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• 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

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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

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• 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