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Topic 6: Case Studies in IUIs
• Recommender Systems
• Machine Learning
• Intelligent Training Interfaces
2
Recommender Systems
• SIRUP: Serendipity in Recommendations via User
Perceptions
Intelligent User Interfaces, 2017
• Many on-demand services for TV content
• Too much time time to choose
• Recommender systems when lacking information
build filter bubbles around users
• There is a strong need for serendipity to keep
people engaged with content
The subjectivity of serendipity depends on:
• the knowledge of the user
• how much the user is keen on knowing more, better
known as curiosity
Curiosity is a strong desire to know or learn something
NOVELTYCHECK
COPING POTENTIALCHECK
level of
CURIOSITYin a TVprogramme
level of
SERENDIPITYcaused by TVprogramme
knowledgeof user
keen on knowingmore
RQ1: Do serendipitous
recommendations trigger
curiosity in users?
Novelty Check
• Linked Open Data paths with cosine similarity measure
• LOD paths allows for innovative connections
• Using types and properties similarity measure
Reggie Yates’s Extreme South
Africa
The Sky at Night
(musical band)
(musical band)
Brian May
Extremeinfluenced by
has member
is presenter of
RQ2: Can the novelty checkof TV programmes be performed with respect to the user
profile using LOD paths components?
Coping Potential Check• Challenging estimation:
• incomplete information about user’s
tastes
• preferences change over time
• unknown attitude towards new content
• Simplified approach:
• count the unique instances of genres
and formats as indicators of the coping
potential
RQ3: Can we
estimate the coping
potential of a user
with the diversity of
genres and formats in
the user profile?
Experiment• 290 British participants: 165 participants’ answers used,
1460 BBC programmes, 8 ratings to build user profile
• favourite genres, formats and demographics
Evaluation of recommendations:
• I did not think of this TV programme, but it seems interesting to me. (Interest)
• This TV programme does not seem interesting to me. (Interest)
• I am surprised to get this TV programme recommended. (Unexpectedness)
• This recommendation fits my personal preferences. (Relevance)
Recommendations
Generation
• Three rankings:
• cosine similarity based on BBC metadata (Baseline)
• cosine similarity based on LOD patterns (SIRUP)
• cosine similarity based on LOD patterns and BBC
metadata
• 2 programmes per intervals (low, medium, high
similarity values)
Results
• Results analysed in different ways:
• Comparison of the distributions of the similarity
values (Wilcoxon Signed Rank test)
• Serendipity (Logistic Regression) Precision
• Catalog coverage
Baseline - BBC Metadata
• Comparison of the distributions of the similarity values:
• interest: the rank of the distribution of the similarity values is low when interest is low
• relevance: the rank of the distribution of the similarity values is low when relevance is low
• unexpectedness: non-significant difference
• Serendipity: non significant model
• Precision:
• 63% for interest
• 64% for relevance
• 67% overall
• Catalog coverage: 35,41%
SIRUP - LOD
Components• Comparison of the distributions of the
similarity values:
• interest: the rank of the distribution of the
similarity values is significantly higher
when interest is high.
• relevance: the rank of the distribution of
the similarity values is significantly higher
when relevance is high.
• unexpectedness: the rank of the
distribution of the similarity values is
significantly lower when unexpectedness
is high.
• Serendipity:
• Estimate Std. Error z value Pr(>|z|)
• (Intercept) -4.0018 0.4325 -9.252 <2e-16
• simValue 2.4372 1.1480 2.123 0.0338
• genre diversity 0.7878 0.3207 2.457
0.0140
• format diversity 0.1742 0.3478 0.501
0.6164
• Precision:
• 68% for interest
• 69% for relevance
• 71% overall
• Catalog coverage: 47,40%
Combined Approach• Comparison of the distributions of the similarity values:
• interest: the rank of the distribution of the similarity values is lower when interest is higher;
• relevance: the rank of the distribution of the similarity values is lower when relevance is low;
• unexpectedness: the rank of the distribution of the similarity values is lower when
unexpectedness is higher.
• Serendipity: non significant model
• Precision:
• 67% for interest
• 65% for relevance
• 69% overall
• Catalog coverage: 34,59%
Machine Learning
• CogniLearn: A Deep Learning-based Interface for
Cognitive Behaviour Assessment
Intelligent User Interfaces, 2017
• Cognitive impairments in early childhood may lead
to poor academic performance
• Research shows that a traditional game of Head-
Shoulders-Knees-Toes can provide psychometric
information leading to behavioural self-regulation
• Visual observation of HSKT can lead to predicting
cognitive behaviour
Method
• Use of Microsoft Kinect V2 Camera
• UI for recording and observing HSKT
• Machine learning techniques on pose estimation
from RGB video streams
Framework
• Deep Learning Architecture exploiting a
Convolutional Neural Network (CNN)
Interface
Experiment
• 15 participants (18-30 years as pilot test beds)
• 60,000 frames of RGB data collected, 4443 frames
annotated
• Dataset available:
http://vlm1.uta.edu/˜srujana/HTKS/CogniLearn_HTK
S_Dataset.html
Intelligent Training Interfaces
• Social Intelligence Modelling using Wearable
Devices
Intelligent User Interfaces, 2017
• Social Signal Processing techniques used to
analyse human behaviour
• Training a computational model to provide feedback
to a public speaker about his co-verbal
communication
• Using wearable devices: smart watch, smart phone,
eye tracking device with microphone.
Social Intelligence Modelling
• Dynamic Bayesian Networks to model complex
temporal relationships between variables
• Machine learning techniques are used to associate
the cognitive state of the public speaker to the
annotated feedback
• Cognitive state influences multimodal behaviour
• Variables include: volume, intonation, speech, gaze fixations, hand gesture
energy, body energy
• Appropriate feedback is a direct consequence of multimodal scores of non-
verbal behaviour
• Appropriate feedback is influenced by the mental state of the user
• Temporal correlation between CS at a certain time, and the previous state of
the user
• For the case studies mentioned, think about the
following questions:
• How can we place the human at the centre of
every day's interaction and task activity?
• How can an interactive system adapt to human
cognitive and emotional factors with the aim to
deliver a personalised and more usable interface?
• What models, architectures and frameworks, do
these case studies use? Discuss them