User Modeling and Recommender Systems at TU München

Flashcards and summaries for User Modeling and Recommender Systems at the TU München

Arrow Arrow

It’s completely free

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

studysmarter schule studium
d

4.5 /5

studysmarter schule studium
d

4.8 /5

Study with flashcards and summaries for the course User Modeling and Recommender Systems at the TU München

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Challenge in measuring dwell time

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

What are advantages of bayesian networks?

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Mobile user profiles: how can it be seamlessly  accessible

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

What is dwell time

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

What is a bayesian network?

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Two main categories of context

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Name examples of UMRS at amazon

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

How can bayes networks be applied for RS?

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Problems with user profile storage

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

What is the definition of a user?

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Pros and Cons of customization

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

Client side dwell time calculation

Your peers in the course User Modeling and Recommender Systems at the TU München create and share summaries, flashcards, study plans and other learning materials with the intelligent StudySmarter learning app.

Get started now!

Flashcard Flashcard

Exemplary flashcards for User Modeling and Recommender Systems at the TU München on StudySmarter:

User Modeling and Recommender Systems

Challenge in measuring dwell time

Multi tabbed browser interfaces, users not actually paying attention to web page
(you don’t know if he is actually using the site or not or if he left it open for a while)

User Modeling and Recommender Systems

What are advantages of bayesian networks?
– provide a natural representation for conditional independence
– allows for representing uncertain obsverations
– topology + CPT = compact representation of jpint distribution
– generally easy for domain experts to construct

User Modeling and Recommender Systems

Mobile user profiles: how can it be seamlessly  accessible

users need to access info across multiple devices seemlessly
solution would be the cloud (server side storage)

User Modeling and Recommender Systems

What is dwell time
  • Amount of time users spend on content items 
  • Important metric to measure user engagement, advanced to just user clicks
  • Could be used as indication for user interest or satisfaction with content
    • more time spends on page means more interest

User Modeling and Recommender Systems

What is a bayesian network?
– Compact specification of full joint distributions
– directed, acycli graph with the following properties:
– set of nodes, one per variable
– a conditional distribution for each node given its parents P(X/Parents(X))
– cinditional distribution represented as a conditional probability table (CPT) giving the distribution over X for each combination of parent values

User Modeling and Recommender Systems

Two main categories of context

Physical context: location, time, data from sensors

Cognitive context: What is user doing right now?

User Modeling and Recommender Systems

Name examples of UMRS at amazon
collaborative filters (only based on info of other user data);
content-based filter (based on item meta data)

User Modeling and Recommender Systems

How can bayes networks be applied for RS?
– well suited for training and learning methods e.g. user marks email message as spam (system can learn)
– good for “per user” approaches -> no pre-defined rules needed -> every user creates own classification of Spam; fits user modeling very well
– causal knowledge usually more robust than inferred knwledge

User Modeling and Recommender Systems

Problems with user profile storage

Transparency issues, the user does not know how much of the information about him/her being store
Control: USer does not have control over the stored info (can’t delete it, change it , etc..)
thats why server side storage is not ideal for user

User Modeling and Recommender Systems

What is the definition of a user?
– interacts with the system
– identification is required
– can act in different roles through pseudonyms and identities

User Modeling and Recommender Systems

Pros and Cons of customization

Pros: adaptable
Cons: Not intelligent nor adaptive

User Modeling and Recommender Systems

Client side dwell time calculation

Utilize JavaScript/DOM events

  • Users must have enabled JavaScript in their browser, data needs to be sent to server

Sign up for free to see all flashcards and summaries for User Modeling and Recommender Systems at the TU München

Singup Image Singup Image
Wave

Other courses from your degree program

For your degree program Management And Technology at the TU München there are already many courses on StudySmarter, waiting for you to join them. Get access to flashcards, summaries, and much more.

Back to TU München overview page

Business Analytics

Investment and Financial Management

Scheduling Lean Manufacturing Systems

International relations

CH2: Consumer Behavior

CH3: Consumer Behavior

CH4: Consumer Behavior

CH5: Consumer Behavior

CH8: Consumer Behavior

CH10: Consumer Behavior

CH11: Consumer Behavior

CH12: Consumer Behavior

Français

Marketing

R Codes

Anorganische

AC Verbindungen_Tutorien

Einsatz und Realisierung von Datenbanken

chemiesoftware

Organische Chemie

Die Chemische Industrie

What is StudySmarter?

What is StudySmarter?

StudySmarter is an intelligent learning tool for students. With StudySmarter you can easily and efficiently create flashcards, summaries, mind maps, study plans and more. Create your own flashcards e.g. for User Modeling and Recommender Systems at the TU München or access thousands of learning materials created by your fellow students. Whether at your own university or at other universities. Hundreds of thousands of students use StudySmarter to efficiently prepare for their exams. Available on the Web, Android & iOS. It’s completely free.

Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards
Awards

Best EdTech Startup in Europe

Awards
Awards

EUROPEAN YOUTH AWARD IN SMART LEARNING

Awards
Awards

BEST EDTECH STARTUP IN GERMANY

Awards

How it works

Top-Image

Get a learning plan

Prepare for all of your exams in time. StudySmarter creates your individual learning plan, tailored to your study type and preferences.

Top-Image

Create flashcards

Create flashcards within seconds with the help of efficient screenshot and marking features. Maximize your comprehension with our intelligent StudySmarter Trainer.

Top-Image

Create summaries

Highlight the most important passages in your learning materials and StudySmarter will create a summary for you. No additional effort required.

Top-Image

Study alone or in a group

StudySmarter automatically finds you a study group. Share flashcards and summaries with your fellow students and get answers to your questions.

Top-Image

Statistics and feedback

Always keep track of your study progress. StudySmarter shows you exactly what you have achieved and what you need to review to achieve your dream grades.

1

Learning Plan

2

Flashcards

3

Summaries

4

Teamwork

5

Feedback