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Adaptive Systeme > Kahoots > Flashcards

Kahoots Flashcards

1
Q

True/False: Adaptivity is user driven personalization

A

false

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2
Q

A system chooses an option and tells the user it did so. This can be described as…

A

Adaptability (more system-driven)

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3
Q

Which adaptation technique is not considered by the traditional adaptive web literature?

a. Content Adaptation
b. Adaptive Navigation
c. Adaptive Interaction
d. Adaptive Presentation

A

c

Adaptive Interaction

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4
Q

Removing text fragments belongs to…

a. Adaptive Navigation
b. Adaptive Presentation
c. Content Adaptation
d. Adaptive Presentation and Adaptive Navigation

A

c

Content Adaptation

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5
Q

The following DOES NOT belong to Adaptive Presentation:

a. Layout Adaption
b. Inserting Fragments
c. Dimming Fragments
d. Zooming

A

b

Inserting Fragments

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6
Q

The following was the last concept to be added to the traditional categorization of adaptive web techniques:

a. Content Adaption
b. Adaptive Presentation Support
c. Adaptive User Interface Generation
d. Adaptive Navigation Support

A

a

Content Adaption

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7
Q

True/False: Workspace awareness mechanisms can be considered adaptive interaction support

A

False

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8
Q

Which of the following statements about the domain model (DM) is wrong?

a. The DM is defined by the entirety of users
b. The DM is usually dynamic
c. A DM comprises several concepts that can be mapped to content
d. The user model might be an overlay over the DM

A

a + b

The DM is defined by the entirety of users
The DM is usually dynamic

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9
Q

True/False: Content-based user modeling could e.g., use meta data about users, features or text.

A

True

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10
Q

True/False: Feature-based user modeling belongs to the collaborative approaches

A

False

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11
Q

True/False: Collaborative user modeling might identify users with similar taste based on their ratings

A

True

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12
Q

Which of the following does not belong to the explicit user information?

a. Evaluation
b. Previously stored information
c. Self assessment
d. Responses to items

A

b

Previously stored information

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13
Q

True/False: Naturally occurring actions are considered implicit user interaction.

A

True

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14
Q

True/False: Collaborative user modeling does not require in-depth personal information about individual users.

A

True

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15
Q

An adaptive interaction system considers a personal minimum input signal duration. This is called…

a. Scan time
b. Lock time
c. Hold time

A

c

Hold time

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16
Q

The following statement describes the carry over effect

a. It is a long-term effect. A community’s search history influences results.
b. It is a short-term effect. A user’s search history influences results.

A

b

It is a short-term effect. A user’s search history influences results.

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17
Q

Which of the following statements about adaptive search is true?

a. Adaptive search might not utilize a non-adaptive search engine.
b. Adaptive search might re-write users’ queries.
c. Adaptive search might re-rank non-personalized search results.
d. Adaptive search tries to consider users’ long- and short-term interests

A

b + c + d

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18
Q

True/False: Stable values in a user model usually change more slowly, compared to ambiguous ones.

A

True

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19
Q

True/False: A personalized lock time describes the time span where no input is taken.

A

True

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20
Q

Google search …

a. has been personalized for more than 15 years.
b. is not subject to the cold start problem
c. mainly considers browsing history as a basis for personalization
d. demonstrates a carry over effect

A

a + c

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21
Q

True/False: A domain-specific ontology can be used as a basis for related user models

A

True

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22
Q

A personalized course website automatically subscribes you to a newsletter series of partner companies. This is…

a. Opt-In
b. Opt-Out

A

b

Opt-Out

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23
Q

One of the following describes that users act rationally when they take privacy-related decisions.

a. Privacy paradox
b. Privacy calculus
c. Privacy pragmatist
d. Privacy fundamentalist

A

b + c

Privacy calculus
Privacy pragmatist

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24
Q

True/False: The privacy paradox connotes that privacy is situational.

A

True

25
Q

Data is not linkable to personal identifiers with reasonable effort. This approach is called…

a. privacy by architecture
b. privacy by decision
c. privacy by policy
d. privacy by chance

A

a

privacy by architecture

26
Q

One of the following is NOT a mechanism to protect user models:

a. Perturbation
b. Justification
c. Distribution
d. Aggregation

A

b

Justification

27
Q

One of the following is NOT a privacy principle

a. Bandwagon
b. Openness
c. Access
d. Accuracy

A

a

Bandwagon

28
Q

A classification process outputs

a. Descriptive models
b. Predictive models

A

b

Predictive models

29
Q

The following outputs descriptive models

a. Classification
b. Clustering
c. Regression

A

b

Clustering

30
Q

True/False: Classification is considered supervised learning.

A

True

31
Q

True/False: Clustering is considered supervised learning

A

False

32
Q

True/False: A classifier’s prediction accuracy might not be helpful if class distribution is unbalanced

A

True

33
Q

The following is true:

a. A classifier’s accuracy is the ratio of correct predictions
b. Recall is a measure of completeness
c. Recall is the ratio of positive instances correctly classified
d. Precision is the ratio of positive instances correctly classified

A

a + b + c

A classifier’s accuracy is the ratio of correct predictions
Recall is a measure of completeness
Recall is the ratio of positive instances correctly classified

34
Q

The following describes overfitting

a. Model fits very specific cases in training data but not general enough
b. Model does not fit specific cases in training data and is too general

A

a

Model fits very specific cases in training data but not general enough

35
Q

The following is true for clustering

a. Classes for data instances are not known in beforehand
b. Might help to derive hidden information from data
c. Classes for data instances are known in beforehand
d. Categorizes data based on a given set of classes

A

a + b

Classes for data instances are not known in beforehand
Might help to derive hidden information from data

36
Q

The Dice coefficient can be described as follows:

a. It is based on an overlap of keywords between documents.
b. It helps to compute similarities between documents.
c. It is based on a weighted average of rating predictions
d. It always considers all documents in a document pool when comparing two.

A

a + b

It is based on an overlap of keywords between documents.
It helps to compute similarities between documents.

37
Q

True/False: TF-IDF can be used to identify the most important keywords in a documents

A

True

38
Q

True/False: TF-IDF considers all documents in a documents pool

A

True

39
Q

True/False: TF-IDF: A word that appears in many documents will be considered more important and get a higher weight

A

False

40
Q

True/False: A word that appears several times in the same documents gets a higher weight.

A

True

41
Q

The user has a specific information need; the system tries to find the most relevant items according to the query. This is…

a. Retrieval
b. Browsing
c. Recommendation

A

a

Retrieval

42
Q

True/False: Retrieval and Browsing have in common, that the user needs to be active

A

True

43
Q

True/False: For a basic collaborative filtering approach we need more than a ratings matrix

A

False

44
Q

The values of cosine similarity range between…

a. 0 and 1 (independent of rating scale used)
b. 0 and 1 (if all ratings are positive values)
c. -1 and 1 (if negative ratings are possible)
d. -1 and 1 (independent of rating scale used)

A

b + c

0 and 1 (if all ratings are positive values)
-1 and 1 (if negative ratings are possible)

45
Q

True/False: Item-based and user-based collaborative filtering use the same algorithms

A

True

46
Q

True/False: Primitive collaborative filtering algorithms can deal well with missing information

A

False

47
Q

Neighbourhood selection in collaborative filtering-based systems is usually based on…

a. a minimum threshold of similarity
b. a minimum number of neighbours
c. a maximum number of neighbours
d. all might be true

A

a + b + c + d

a. a minimum threshold of similarity
b. a minimum number of neighbours
c. a maximum number of neighbours
d. all might be true

48
Q

True/False: Slope One can deal better with missing ratings, compared to primitive collaborative filtering approaches

A

True

49
Q

A filter bubble…

a. occurs if a system only recommends items with the highest prediction
b. isolates users from information outside known interests
c. can influence opinion making in a community
d. is a form of data sparsity

A

a + b + c

occurs if a system only recommends items with the highest prediction
isolates users from information outside known interests
can influence opinion making in a community

50
Q

True/False: The cold start problem is a form of data sparsity

A

True

51
Q

The following does not belong to adaptive interaction support:

a. Adaptive User Interface Element Arrangement
b. Adaptive Selection of Input Devices
c. Adaptive Team Composition
d. Adaptive Configuration of Input Devices

A

c

Adaptive Team Composition

52
Q

True/False: The adaptation model (AM) is linked to both the user model (UM) and the domain model (DM).

A

True

53
Q

Which of the following is NOT a vulnerability specific to adaptive systems?

a. Latent access to personal information
b. Injection of malicious code
c. Reverse engineering of personalization
d. Profile injection attacks

A

b

Injection of malicious code

54
Q

True/False: A profile injection attack could be used to push or denigrate an item

A

True

55
Q

A bandwagon attack…

a. aims at denigrating a certain product
b. requires technical rather than domain knowledge
c. associates a certain item with a more prominent one.
d. relies on items that are regularly rated.

A

c + d

associates a certain item with a more prominent one.
relies on items that are regularly rated.

56
Q

One of the following is an OPTIMISTIC approach to counteract profile injection attacks.

a. Analysis of log data to identify potential attacks
b. Increase effort involved with provision of ratings

A

a

Analysis of log data to identify potential attacks

57
Q

One of the following is a PESSIMISTIC approach to counteract profile injection attacks.

a. Consider ratings for recommendations only after a while of interaction
b. Remove ratings that users report as suspicious

A

a

Consider ratings for recommendations only after a while of interaction

58
Q

True/False: Privacy Pragmatists are willing to disclose data when they see benefit and understand the reasons for data collection.

A

True

Adaptive Systeme (8 decks)

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