Collab at TU Kaiserslautern

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blindness while moving the eye

Exemplary flashcards for Collab at the TU Kaiserslautern on StudySmarter:

Grundlagen von Recommender Systemen 

Users

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semantic or contextual closeness / contextual search

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Learning-Assistant with spaced repetition algorithm

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Eye tracking technical overview

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Recommender Systems 

Why do we need filtering?

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Controlled vocabularies / thesaur

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How can we evaluate a search (information retrieval) system

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Learning-Assistant with spaced repetition algorithm

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Inverted index with term weights

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Relevance Feedback (or Pseudo Relevance Feedback)

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5 Steps of Relevance Feedback

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Gaze-Based Self-Condence Estimation Research Hypotheses: 

● RH1: Questions answered correctly without confidence tend to be forgotten compared to knowledge with confidence

● RH2: Questions answered incorrectly with confidence tend to be mistaken again compared to wrong knowledge without confidence 

● RH3: Estimating self-confidence from learning behaviors and giving feedback (e.g., adding questions to a review list, highlighting them while reviewing) avoids such scenarios.

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Recommender systems 

Definition

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Exemplary flashcards for Collab at the TU Kaiserslautern on StudySmarter:

Collab

blindness while moving the eye

saccadic suppression

Collab

Grundlagen von Recommender Systemen 

Users

Users - Features 

  • Aufbau: Name des Features + Ausprägung 
  • Klassische Features: 
    •  Demografisch: Alter, Geschlecht, …  
    • Ort: Geburtsort, aktuelle Adresse, … 
    • Psychologische Features: Interessen, Hobbies, Musikgeschmack …

Collab

semantic or contextual closeness / contextual search

● This idea is based on the assumption that two terms, which frequently occur in the same context, are associatively related to each other 

● As a consequence we statistically measure the co-occurrences of two words, which is called contextual search

Collab

Eye tracking technical overview

● Gaze data is collected using either a remote or head-mounted eye-tracker 

● A (infrared) light source is directed toward the eye 

● Camera tracks the reflection of the light source along with visible ocular features 

● Data is used to extrapolate eye rotation as well as the direction of gaze 

● Visual path is analyzed across an interface and transformed into a set of pixel coordinates, i.e. 

○ which features are seen, when a particular feature captures attention, how quick is the eye moving, what content is overlooked, etc.

Collab

Recommender Systems 

Why do we need filtering?

● Catalogue size (e.g. iTunes: 6 Million songs in 2009!) 

● Several million new songs each day 

● Long Tail Effect: “Music Overload” generates demand for filtering, recommendation, personalization

Collab

Controlled vocabularies / thesaur

Möglichkeiten das Ergebnis der Suche zu verbessern: 

→ Metadaten: Entweder manuell oder automatisiert zu Dokumenten hinzugefügt 

● using it to “expand the semantic nature of a query” as “data about data” 

● using it to annotate the documents in a repository with useful information about e.g. their content, purpose and origin 

● improving classification, ranking and relevance by exploiting the content of these “tags” and use the information stored in them

Collab

How can we evaluate a search (information retrieval) system

Precision + Recall // F-Measure

Collab

Inverted index with term weights

A term vector may contain just the value “true” as an indicator that a word is part of a document or may show a weight, e.g. according to the frequency of their occurrence in a document (Assign weights instead of boolean values) 

→ The importance of an index term can be expressed by its weight x 

→ Easy processing of a query, e.g. by summarizing the weights of the terms of a conjunction

Collab

Relevance Feedback (or Pseudo Relevance Feedback)

The idea of the approach is to involve the user‘s preference in the retrieval process in order to improve the ranking of the results → i.e. the user gives feedback on the relevance of documents in an initial set of results.

Collab

5 Steps of Relevance Feedback

1. The user issues a (short, simple) query 

2. The system returns an first result list retrieved from a given corpus. 

3. The user labels some returned documents as relevant or non relevant. 

4. The system computes a better representation of the information need based on the user feedback. 

5. The system displays a revised set of retrieval results.

Collab

Gaze-Based Self-Condence Estimation Research Hypotheses: 

● RH1: Questions answered correctly without confidence tend to be forgotten compared to knowledge with confidence

● RH2: Questions answered incorrectly with confidence tend to be mistaken again compared to wrong knowledge without confidence 

● RH3: Estimating self-confidence from learning behaviors and giving feedback (e.g., adding questions to a review list, highlighting them while reviewing) avoids such scenarios.

● RH1: Questions answered correctly without confidence tend to be forgotten compared to knowledge with confidence. → True 

● RH2: Questions answered incorrectly with confidence tend to be mistaken again compared to wrong knowledge without confidence. → Not always true 

● RH3: Estimating self-confidence from learning behaviors and giving feedback (e.g., adding questions to a review list, highlighting them while reviewing) avoids such scenarios. → True

Collab

Recommender systems 

Definition

A recommender system R is a program which recommends to user Ui a set of items Ij according to his preferences.

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Social Collaboration Mnagment at

Technische Hochschule Mittelhessen

Controlling at

Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen

Controlling at

Hochschule für Technik, Wirtschaft und Kultur Leipzig

Controlling at

Fachhochschule Campus 02 Graz

Controlling at

LMU München

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