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DSS > Chapter 5 > Flashcards

Chapter 5 Flashcards

1
Q

What are the two types of filtering?

A
  1. Content-Based
  2. Collaborative
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2
Q

Content-Based Filtering: Idea

A

recommends items based on their features and the user’s preferences for those features

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3
Q

Content-Based Filtering: on what does it rely?

A

on a profile of the user’s prefferneces

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4
Q

Content-Based Filtering: Process

A
  • build a profile for each user based on their preferences for different features of items
  • recommend items that match the user’s profile and have features similar to what they have liked in the past
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5
Q

Content-Based Filtering: Pros

A
  • addresses the cold start problem for new items
  • can provide explanations fo recommendations based on item features
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6
Q

Content-Based Filtering: Cons

A
  • may not capture complex user preferences that go beyond item features
  • requires detailed information about the items and user preferences for features
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7
Q

Collaborative Filtering: Idea

A

makes recommendations based on user behavior and preferences

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8
Q

What does Collaborative Filtering assume?

A

that users who have agreed in the past tend to agree in the future

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9
Q

What are the types of Collaborative Filtering?

A
  1. User-Based
  2. Item-Based
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10
Q

How does User-Based Collabroative Filtering recommend items?

A

based on the preferences of users who are similar to the target user

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11
Q

How does Item-Based Collaborative Filtering recommend items?

A

it recommends items that are similar to those liked by the target user

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12
Q

Collaborative Filtering: Pros

A
  • does not require knowledge of item features
  • can capture complex patterns and user preferences
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13
Q

Collaborative Filtering: Cons

A
  • cold start problem
  • sparsity
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14
Q

Define the cold start problem

A

it can be challenging for Collaborative Filtering to provide accurate recommendations for new users or items with little or no history

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15
Q

Define Sparsity

A

Collaborative Filtering: when dealing with a large number of users and items, the user-item interaction matrix can be sparse, making it difficult to find similar users or items

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16
Q

Define User based collaborative filtering

A

similar tastes in the past
will have similar tastes in the future

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17
Q

Formula pred(Alice,Item5)

A
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18
Q

Formula sim(Item5, Item4)

A
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19
Q

Commonly used techniques of content-based filtering

A
  1. TF-IDF
  2. Clustering
  3. Decision trees
  4. ANN
  5. Bayesian networks
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19
Q

Well known problems of content-based filtering

A
  1. Cold-start problem for new users
  2. Cold-start problem for new items
  3. Scalability
  4. Sparsity problem
  5. Exotic profiles
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20
Q

Chatbot definition

A

computerized service that enables easy conversations between humans and humanlike computerized robots

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21
Q

Process of chatting with bots (graph)

A
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22
Q

Chatbots: categories

A
  1. enterprise chatbots
  2. virtual personal assistants
    - Alexda
    - Apple Siri
    - Cortana
  3. Robo-advisors
    - financial advisor
    - shopping advisor
    - travel
    - medical advisor
23
Q

Chatbot: IBM estimates

A

265 billion service requests every year, addressing them costs companies 1.3 trillion dollars

24
Q

What percentage of customer interactions wil be AI-powered by 2025?

A

95%

25
Q

What percentage of customer service encounters in 2020 do not involve humans on the firm side?

A

85%

26
Q

What is a chatbot building platform?

A

a chatbot development platform that does not require coding skills of developers

27
Q

Definition of DSS by Gorry and Scott-Morton

A

interactive computer based systems which support decision makers by utilization of data and models to solve semistructured problems

28
Q

List components of Decision Support Systems

A
  • interactive computer-based systems
  • decision makers
  • decision support
  • data
  • models
  • decision problems
29
Q

What are the three staged of DSS as Judge-Advisor Systems?

A
  1. Input
  2. Process
  3. Output
30
Q

Of what does the Input (stage of DSS as Judge-Advisor Systems) consist?

A
  • advisor characteristics
  • judge characteristics
  • environment characteristics
31
Q

Of what does the Process stage of DSS as Judge-Advisor Systems) consist?

A
  • format of advice
  • type of interaction
  • explanation of advice
32
Q

Of what does the Output stage of DSS as Judge-Advisor Systems) consist?

A
  • trust
  • accuracy
  • confidence
  • advice utilization
  • system satisfaction
  • intention to continue (use)
33
Q

What is a Judge?

A

decision maker

34
Q

What could an Advisor be?

A
  • human
    -alogirthm
  • human-algorithm team
35
Q

What is Advice?

A

recommendation favoring or discouraging particular option(s)

36
Q

What does Algorithm Aversion mean?

A

reluctance of human decision makers to use superior but imperfect algorithms

37
Q

Why do people often chose human forecasters voer statistical algorithms?

A
  • they lose confidence in algorithmic forecasters after seeing them make the same mistakes
  • they would rather choose human even if the algorithm outperforms
38
Q

What is algorithm appreciation?

A

adherence to advice when it comes from an algorithm than from a person

39
Q

Measurement of algorithmic literacy (chart)

A
40
Q

What is the solution to the problem: lack of decision control

A

human-in-the-loop decision making

41
Q

What is the solution to the problem: lack of incentivization

A

behavioral design

42
Q

What is the solution to the problem: combating intuition

A

engaging intuition

43
Q

Describe algorithmic bias, fainress and transparency

A
  • ai based predicions are faster, cheaper, more reliable and scalable
  • unintended effects: discrimination or racism
44
Q
A
45
Q

What are sources for bias?

A
  • biased training sets
  • algorithm itself
  • presentation formats
  • users
45
Q

What are legal, privacy and ethical issues?

A
  • everyone may be affected by these applciations
  • doable does not equal appropriate, legal or ethical
  • data science and AI professionals/,amager must be aware of these concerns
45
Q

What are corrective actions (for legal problems)?

A
  • use unbiased data
  • mandatory data governance
  • model evaluation by social groups
  • explainable AI/machine learning: from black box to glass box
46
Q

Dark side of analytics; legal question

A

Who is liable for wrong advice?

47
Q

Dark side of analytics: legal issues

A
  • who owns the knowledge in a knowledge base?
  • can management force experts to contribute to their expertise to an intelligent system?
48
Q

Define Privacy

A

the right to be left alone and the right to be free from unreasonable personal intrusions

49
Q

Is the right of privacy Absolut?

A

No

50
Q

Which one is superior: the public’s right to know or the individual’s right to privacy?

A

the public’s right to know

51
Q

What is AI’s impact on jobs?

A
  • cuts jobs
  • cuts opportunity
52
Q

How can one deal with change

A
  • use learning and education to facilitate the change
  • involve the private sector in enhancing retraining
  • have governments provide incentives to the private sector to improve human capital
  • encourage private and public sectors to create appropiate digital infrastructure
  • develop innovative income and wage schemes
  • carefully plan the transition to the new work
  • deal properly with displaced employees

DSS (4 decks)

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