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PARADIGM SHIFTS
IN USER MODELING
A Journey from
Historical Foundations
to Emerging Trends
TUTORIAL
J u l y 1 , 2 0 24
ABOUT US
A B O U T U S
Erasmo Purificato
Ph.D. Candidate
Otto von Guericke University Magdeburg, Germany
erasmo.purificato@acm.org
A B O U T U S
Ludovico Boratto
Associate Professor
University of Cagliari, Italy
ludovico.boratto@acm.org
A B O U T U S
Ernesto William De Luca
Full Professor
Otto von Guericke University Magdeburg, Germany
ernesto.deluca@ovgu.de
A G E N D A
HISTORICAL
OVERVIEW
[Purificato et al., 2024, §1.1]
“USER MODELING” NOTION INTRODUCED
Pioneering work by Allen, Cohen, Perrault, and Rich
[Perrault et al., 1978; Cohen and Perrault, 1979; Rich, 1979]
set the stage for the research in this field.
In these early models, there is no distinct
separation between the components used for
user modeling and those used for other functions
[Sleeman, 1985; Allgayer et al., 1989; Wahlster and Kobsa, 1989].
1970s
[Purificato et al., 2024, §1.1]
STEREOTYPE USER MODELING
Constituted the first attempt to differentiate
a user from other users [Rich, 1979]
and inspired several future contributions
[Ardissono and Sestero, 1995; Krulwich, 1997].
1970s
[Purificato et al., 2024, §1.1]
GENERIC USER MODELING SYSTEMS
GUMS operate as independent components
within a system during runtime [Finin and Drager, 1986].
Primary GUMS established the foundation for
fundamental systems, such as UMT [Brajnik and Tasso, 1994],
TAGUS [Paiva and Self, 1994], BGP-MS [Kobsa and Pohl, 1994],
Doppelänger [Orwant, 1994], and the Um toolkit [Kay, 1995].
1980s
[Purificato et al., 2024, §1.1]
USER MODELING SHELL SYSTEMS
Usually considered equivalent to GUMS,
they are introduced to support complex reasoning
about a user (especially in domains where
characteristics are clearly identified) with
the aim to be broadly adaptable [Kobsa, 1990].
1980s
[Purificato et al., 2024, §1.1]
USER MODELING FOR
WEB PERSONALIZATION
The involvement of user modelling is crucial for
the advent of personalization and the transition from
anonymous mass marketing and sales to individualized
one-to-one marketing approaches on the Internet
[Peppers and Rogers, 1993; Caglayan et al., 1997; Konstan et al., 1997;
Fink and Kobsa, 2000; Kay et al., 2002; Brusilovsky, 2004].
1990s
[Purificato et al., 2024, §1.1]
COMMERCIAL USER MODELING SERVERS
They maintain a user model as a centralized
repository, shared across several applications
through a flexible client-server architecture
[Kobsa, 2001; Kay et al., 2002; Trella et al., 2003; Fink, 2004;
Brusilovsky, 2004; Kobsa and Fink, 2006; Kobsa, 2007].
1990s
[Purificato et al., 2024, §1.1]
ADAPTIVE HYPERMEDIA
Intersection of hypermedia systems [Kobsa et al., 2001]
and adaptive user interfaces [Langley, 1999].
Adaptive hypermedia [Brusilovsky et al., 1998; Brusilovsky, 2001]
tailored what the user is offered based on a model of
the user’s goals, preferences, and knowledge.
[De Bra et al., 1999; Brusilovsky and Maybury, 2002].
2000s
[Purificato et al., 2024, §1.1]
ONTOLOGY-BASED USER MODELING
The advent of the semantic web prompted
investigations into representing and
modeling user preferences through ontologies,
employed to semantically organize
and connect user profiles
[Middleton et al., 2004; Mehta et al., 2005; Sieg et al., 2007;
De Luca et al., 2010; Sosnovsky and Dicheva, 2010].
2000s
[Purificato et al., 2024, §1.1]
EXPERT FINDING AND EXPERT PROFILING
These tracks within the Enterprise Track at
TREC 2005 [Craswell et al., 2005] constitutes a significant
turning point in user modeling and profiling research.
Advent of expertise retrieval research area [Balog et al., 2007].
2000s
[Purificato et al., 2024, §1.1]
RISE OF BEHAVIOR MODELING
Emphasis on personalization in various digital services,
particularly in recommenders, where researchers
developed advanced algorithms to analyze user behavior
and preferences for improved content personalization
[Abel et al., 2011; Lakiotaki et al., 2011;
Masthoff, 2011; Konstan and Riedl, 2012].
2010-2015
[Purificato et al., 2024, §1.1]
CONTEXT-AWARE USER MODELING
Aim to understand how user preferences and
behaviors change in different contexts.
This included factors such as location, time, and device,
leading to more adaptive and responsive system
[Adomavicius and Tuzhilin, 2011; Verbert et al., 2012;
Said et al., 2013; Codina et al., 2015].
2010-2015
[Purificato et al., 2024, §1.1]
DATA MINING APPROACHES
The ascent of big data drove the investigation of
advanced data mining techniques for user modeling
[Romero and Ventura, 2013; D’Oca and Hong, 2014;
van Dam and van de Velden, 2015].
2010-2015
[Purificato et al., 2024, §1.1]
MACHINE LEARNING AND
DEEP LEARNING APPROACHES
Large datasets have witnessed the application of
ML algorithms [Mercado et al., 2016; Shin, 2016;
Krishnan and Kamath, 2017; Lin et al., 2019]
and DL models [Gu et al., 2020; Wen et al., 2021;
Li et al., 2022; Wei et al., 2022] to unveil meaningful
patterns into user behaviors and automatically
learn user representations from raw data.
2016-2024
[Purificato et al., 2024, §1.1]
MULTIMODAL USER MODELS
These models aim to attain a comprehensive
understanding of user preferences and behaviors
by integrating information from various modalities
[Saevanee et al., 2015; Farseev et al., 2015; Guo et al., 2018].
2016-2024
[Purificato et al., 2024, §1.1]
ETHICAL CONSIDERATIONS AND
BEYOND-ACCURACY APPROACHES
Recent years have seen increasing needs
for privacy [Wu et al., 2021; Raber and Krüger, 2022; Liu et al., 2023],
transparency [Balog et al., 2019; Huang et al., 2019; Guesmi et al., 2022],
equity and fairness [Dai and Wang, 2021; Purificato et al., 2022;
Zheng et al., 2022; Abdelrazek et al., 2023; Celikok et al., 2023].
2016-2024
[Purificato et al., 2024, §1.1]
[Purificato et al., 2024, §2]
A representation of the preferences of any individual
user; roughly, it is a structured representation of the
user’s needs through which a retrieval system should,
e.g., act upon one or more goals based on that profile
and autonomously, pursuing the goals posed by the use.
[Amato and Straccia, 1999]
US E R PR O F I L E
The procedure for gathering information on the
user’s interest; the system utilizes such information to
tailor services and improve the user’s satisfaction.
[Kanoje et al., 2015]
US E R PR O F I L E
[Purificato et al., 2024, §2]
A representation of information about an
individual user that is essential for an adaptive
system to provide the adaptation effect.
[Brusilovsky et al., 2007]
US E R MO D E L
A data structure that is used to capture specific
characteristics about an individual user.
[Piao and Breslin, 2018]
US E R MO D E L
[Purificato et al., 2024, §2]
The process of acquiring, extracting,
and representing the features of users.
[Zhou et al., 2012]
US E R PR O F I L I N G US E R PR O F I L I N G
The process of inferring an individual’s interests,
personality traits, or behaviors from generated data
to create an efficient user representation, which
is exploited by adaptive and personalized systems.
[Eke et al., 2019]
[Purificato et al., 2024, §2]
US E R PR O F I L I N G
The process of automatically converting user information
into a predefined and interpretable format that reflects the
most important aspects of the user’s profile, which are
useful for further decision-making in practical applications.
[Vo et al., 2021]
US E R MO D E L I N G
The process of gathering information about a user’s
interests, constructing, maintaining, and using user profiles.
[Farid et al., 2018]
[Purificato et al., 2024, §2]
The process of building up and modifying a
conceptual understanding of the user. Its task is to
learn a latent representation for each user, with the
help of items and item features, with applications to
response prediction, recommendation, and other.
[Li and Zhao, 2020]
US E R PR O F I L E MO D E L I N G
The process that constitutes the methodology for
building a user profile; it requires two steps to
describe: “what” has to be represented, and
“how” this information is effectively represented.
[Amato and Straccia, 1999]
US E R MO D E L I N G
[Purificato et al., 2024, §2]
A user model (or user profile) is a structured
representation of an individual user’s
preferences, needs, behaviors, and demographic
details to personalize system interactions.
It is derived from direct user feedback or
inferred through machine learning and data
mining techniques. It supports the predictions of
future user intentions and the refinement of
systems response to enhance user satisfaction.
User models are often instrumental in optimizing
the relevance and efficiency of adaptive
systems, ensuring that user interactions are
aligned with individual needs and preferences.
[Purificato et al., 2024]
NOV E L DE F I N I T I O N S
[Purificato et al., 2024, §2]
NOV E L DE F I N I T I O N S
User modeling (or user profiling) is the process of
acquiring, extracting, and representing user features
and personal characteristics to build accurate user
models (or user profiles).
It encompasses inferring personality traits and
behaviors from user-generated data. This dynamic
practice includes automatically converting user
information into interpretable formats, capturing latent
interests, and learning conceptual user representations.
Essentially, user modeling constitutes the methodology
for building and modifying user models, determining
“what” to represent and “how” to effectively represent
this information for adaptive and personalized systems.
[Purificato et al., 2024]
[Purificato et al., 2024, §2]
PARADIGM
SHIFTS AND
NOVEL TRENDS
[Purificato et al., 2024, §3]
EXPLICIT USER MODELING
Also known as static or factual modeling,
required direct input from the user,
such as filling out a questionnaire or
completing an online form.
[Purificato et al., 2024, §3]
IMPLICIT USER MODELING
Initially used together with explicit methods,
modern systems shifted to passive collection and
analysis of dynamic user data, thus called
behavioral and adaptive modeling.
Static data still used by exploiting information
previously shared (e.g., social network accounts)
defining the novel pseudo-explicit user modeling.
[Purificato et al., 2024, §3]
DIRECT USER PREFERENCES
Traditionally, user preferences
and interests have been modelled
using explicit and direct feedback.
[Purificato et al., 2024, §3]
INDIRECT USER PREFERENCES
Everyday use of digital platforms and
reluctance of users in providing direct
feedback led to a growing emphasis on
capturing an individual’s preferences and
interests hidden in users’ historical behaviors.
[Purificato et al., 2024, §3]
USER
BEHAVIORAL
MODELING
MULTI-BEHAVIOR MODELING
Integrates various forms of user
interactions with items, rather than
relying on a single type of interaction.
MICRO AND MACRO
BEHAVIORAL MODELING
Respectively, the immediate actions that
a user takes reflecting short-term
preferences, and large-scale actions that
reflect a user’s long-term commitment.
SEQUENTIAL BEHAVIOR MODELING
Considers the order and timing of user actions
as influential for modifying user interests.
HIERARCHICAL USER PROFILING
Models users’ real-time interests at
various levels of granularity.
MOBILE USER PROFILING
Involves discerning users’ interests
and behavioral patterns based on
their activities on mobile devices.
[Purificato et al., 2024, §3]
SPECIFIC USER
REPRESENTATION
Scarcity of studies on generalized
user model representation.
Researchers tend to focus on
specific aspects of user modeling
rather than a holistic approach.
[Purificato et al., 2024, §3]
UNIVERSAL USER
REPRESENTATION
Create a unified and generalized
profile of a user by encapsulating
a broad spectrum of user behaviors
and preferences without bias toward
any specific task.
[Purificato et al., 2024, §3]
HOLISTIC USER MODELING
Integrates diverse and heterogeneous
personal data sources to construct a
comprehensive user representation.
[Purificato et al., 2024, §3]
GRAPH
DATA
STRUCTURES
KNOWLEDGE GRAPHS
Specific type of graph structure that
effectively represents complex information
by accumulating and conveying knowledge
of the real world, making them particularly
useful in several contexts.
GRAPH STRUCTURES
In the context of user modeling, graph
structures can be used to represent user
behavior, preferences, and interactions by
leveraging nodes and edges as, respectively,
the users and the relationships among them.
[Purificato et al., 2024, §3]
DEEP
LEARNING
These models have significantly
contributed to advancements in user
modeling research field, enabling more
accurate and comprehensive profiling
and prediction of user behavior.
Attention
Mechanism
Convolutional
Neural Networks
Autoencoders
Graph Neural
Networks
Recurrent
Neural Networks
Transformers
Long-Short
Term Memory
[Purificato et al., 2024, §3]
STANDARD APPLICATIONS
Versatile and valuable approaches
in many fields, particularly where
user-specific services are crucial, e.g.:
o Recommender Systems; ;
o E-commerce and Marketing;
o User Interface Adaptation. .
[Purificato et al., 2024, §3]
CONTEMPORARY APPLICATIONS
Innovative approaches applied in
various important research fields to
tackle state-of-the-art challenges, e.g.:
o Fake news detections;
o Social Network analysis;
o Cybersecurity.
[Purificato et al., 2024, §3]
BEYOND-
ACCURACY
PERSPECTIVES
Similar to the transformation observed in deep learning,
the incorporation of advanced techniques extending
beyond mere accuracy marks a significant global shift
in various domains, including user modeling. These
approaches prioritize fundamental values for humans,
e.g., privacy, fairness, and transparency.
[Purificato et al., 2024, §3]
Special Issue
“User Perspectives in
Human-Centered
Artificial Intelligence”
International Journal of
Human-Computer Studies (Q1)
Call for papers
Submission deadline:
November 18, 2024
CONTACTS
Erasmo Purificato
erasm o. p urificat o@ acm .org
Ludovico Boratto
ludo vico.b orat t o@ acm .o rg
Ernesto W. De Luca
ern est o. deluca@ ovgu.de
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Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends

  • 1. PARADIGM SHIFTS IN USER MODELING A Journey from Historical Foundations to Emerging Trends TUTORIAL J u l y 1 , 2 0 24
  • 3. A B O U T U S Erasmo Purificato Ph.D. Candidate Otto von Guericke University Magdeburg, Germany erasmo.purificato@acm.org
  • 4. A B O U T U S Ludovico Boratto Associate Professor University of Cagliari, Italy ludovico.boratto@acm.org
  • 5. A B O U T U S Ernesto William De Luca Full Professor Otto von Guericke University Magdeburg, Germany ernesto.deluca@ovgu.de
  • 6. A G E N D A
  • 8. “USER MODELING” NOTION INTRODUCED Pioneering work by Allen, Cohen, Perrault, and Rich [Perrault et al., 1978; Cohen and Perrault, 1979; Rich, 1979] set the stage for the research in this field. In these early models, there is no distinct separation between the components used for user modeling and those used for other functions [Sleeman, 1985; Allgayer et al., 1989; Wahlster and Kobsa, 1989]. 1970s [Purificato et al., 2024, §1.1]
  • 9. STEREOTYPE USER MODELING Constituted the first attempt to differentiate a user from other users [Rich, 1979] and inspired several future contributions [Ardissono and Sestero, 1995; Krulwich, 1997]. 1970s [Purificato et al., 2024, §1.1]
  • 10. GENERIC USER MODELING SYSTEMS GUMS operate as independent components within a system during runtime [Finin and Drager, 1986]. Primary GUMS established the foundation for fundamental systems, such as UMT [Brajnik and Tasso, 1994], TAGUS [Paiva and Self, 1994], BGP-MS [Kobsa and Pohl, 1994], Doppelänger [Orwant, 1994], and the Um toolkit [Kay, 1995]. 1980s [Purificato et al., 2024, §1.1]
  • 11. USER MODELING SHELL SYSTEMS Usually considered equivalent to GUMS, they are introduced to support complex reasoning about a user (especially in domains where characteristics are clearly identified) with the aim to be broadly adaptable [Kobsa, 1990]. 1980s [Purificato et al., 2024, §1.1]
  • 12. USER MODELING FOR WEB PERSONALIZATION The involvement of user modelling is crucial for the advent of personalization and the transition from anonymous mass marketing and sales to individualized one-to-one marketing approaches on the Internet [Peppers and Rogers, 1993; Caglayan et al., 1997; Konstan et al., 1997; Fink and Kobsa, 2000; Kay et al., 2002; Brusilovsky, 2004]. 1990s [Purificato et al., 2024, §1.1]
  • 13. COMMERCIAL USER MODELING SERVERS They maintain a user model as a centralized repository, shared across several applications through a flexible client-server architecture [Kobsa, 2001; Kay et al., 2002; Trella et al., 2003; Fink, 2004; Brusilovsky, 2004; Kobsa and Fink, 2006; Kobsa, 2007]. 1990s [Purificato et al., 2024, §1.1]
  • 14. ADAPTIVE HYPERMEDIA Intersection of hypermedia systems [Kobsa et al., 2001] and adaptive user interfaces [Langley, 1999]. Adaptive hypermedia [Brusilovsky et al., 1998; Brusilovsky, 2001] tailored what the user is offered based on a model of the user’s goals, preferences, and knowledge. [De Bra et al., 1999; Brusilovsky and Maybury, 2002]. 2000s [Purificato et al., 2024, §1.1]
  • 15. ONTOLOGY-BASED USER MODELING The advent of the semantic web prompted investigations into representing and modeling user preferences through ontologies, employed to semantically organize and connect user profiles [Middleton et al., 2004; Mehta et al., 2005; Sieg et al., 2007; De Luca et al., 2010; Sosnovsky and Dicheva, 2010]. 2000s [Purificato et al., 2024, §1.1]
  • 16. EXPERT FINDING AND EXPERT PROFILING These tracks within the Enterprise Track at TREC 2005 [Craswell et al., 2005] constitutes a significant turning point in user modeling and profiling research. Advent of expertise retrieval research area [Balog et al., 2007]. 2000s [Purificato et al., 2024, §1.1]
  • 17. RISE OF BEHAVIOR MODELING Emphasis on personalization in various digital services, particularly in recommenders, where researchers developed advanced algorithms to analyze user behavior and preferences for improved content personalization [Abel et al., 2011; Lakiotaki et al., 2011; Masthoff, 2011; Konstan and Riedl, 2012]. 2010-2015 [Purificato et al., 2024, §1.1]
  • 18. CONTEXT-AWARE USER MODELING Aim to understand how user preferences and behaviors change in different contexts. This included factors such as location, time, and device, leading to more adaptive and responsive system [Adomavicius and Tuzhilin, 2011; Verbert et al., 2012; Said et al., 2013; Codina et al., 2015]. 2010-2015 [Purificato et al., 2024, §1.1]
  • 19. DATA MINING APPROACHES The ascent of big data drove the investigation of advanced data mining techniques for user modeling [Romero and Ventura, 2013; D’Oca and Hong, 2014; van Dam and van de Velden, 2015]. 2010-2015 [Purificato et al., 2024, §1.1]
  • 20. MACHINE LEARNING AND DEEP LEARNING APPROACHES Large datasets have witnessed the application of ML algorithms [Mercado et al., 2016; Shin, 2016; Krishnan and Kamath, 2017; Lin et al., 2019] and DL models [Gu et al., 2020; Wen et al., 2021; Li et al., 2022; Wei et al., 2022] to unveil meaningful patterns into user behaviors and automatically learn user representations from raw data. 2016-2024 [Purificato et al., 2024, §1.1]
  • 21. MULTIMODAL USER MODELS These models aim to attain a comprehensive understanding of user preferences and behaviors by integrating information from various modalities [Saevanee et al., 2015; Farseev et al., 2015; Guo et al., 2018]. 2016-2024 [Purificato et al., 2024, §1.1]
  • 22. ETHICAL CONSIDERATIONS AND BEYOND-ACCURACY APPROACHES Recent years have seen increasing needs for privacy [Wu et al., 2021; Raber and Krüger, 2022; Liu et al., 2023], transparency [Balog et al., 2019; Huang et al., 2019; Guesmi et al., 2022], equity and fairness [Dai and Wang, 2021; Purificato et al., 2022; Zheng et al., 2022; Abdelrazek et al., 2023; Celikok et al., 2023]. 2016-2024 [Purificato et al., 2024, §1.1]
  • 23. [Purificato et al., 2024, §2]
  • 24. A representation of the preferences of any individual user; roughly, it is a structured representation of the user’s needs through which a retrieval system should, e.g., act upon one or more goals based on that profile and autonomously, pursuing the goals posed by the use. [Amato and Straccia, 1999] US E R PR O F I L E The procedure for gathering information on the user’s interest; the system utilizes such information to tailor services and improve the user’s satisfaction. [Kanoje et al., 2015] US E R PR O F I L E [Purificato et al., 2024, §2]
  • 25. A representation of information about an individual user that is essential for an adaptive system to provide the adaptation effect. [Brusilovsky et al., 2007] US E R MO D E L A data structure that is used to capture specific characteristics about an individual user. [Piao and Breslin, 2018] US E R MO D E L [Purificato et al., 2024, §2]
  • 26. The process of acquiring, extracting, and representing the features of users. [Zhou et al., 2012] US E R PR O F I L I N G US E R PR O F I L I N G The process of inferring an individual’s interests, personality traits, or behaviors from generated data to create an efficient user representation, which is exploited by adaptive and personalized systems. [Eke et al., 2019] [Purificato et al., 2024, §2]
  • 27. US E R PR O F I L I N G The process of automatically converting user information into a predefined and interpretable format that reflects the most important aspects of the user’s profile, which are useful for further decision-making in practical applications. [Vo et al., 2021] US E R MO D E L I N G The process of gathering information about a user’s interests, constructing, maintaining, and using user profiles. [Farid et al., 2018] [Purificato et al., 2024, §2]
  • 28. The process of building up and modifying a conceptual understanding of the user. Its task is to learn a latent representation for each user, with the help of items and item features, with applications to response prediction, recommendation, and other. [Li and Zhao, 2020] US E R PR O F I L E MO D E L I N G The process that constitutes the methodology for building a user profile; it requires two steps to describe: “what” has to be represented, and “how” this information is effectively represented. [Amato and Straccia, 1999] US E R MO D E L I N G [Purificato et al., 2024, §2]
  • 29. A user model (or user profile) is a structured representation of an individual user’s preferences, needs, behaviors, and demographic details to personalize system interactions. It is derived from direct user feedback or inferred through machine learning and data mining techniques. It supports the predictions of future user intentions and the refinement of systems response to enhance user satisfaction. User models are often instrumental in optimizing the relevance and efficiency of adaptive systems, ensuring that user interactions are aligned with individual needs and preferences. [Purificato et al., 2024] NOV E L DE F I N I T I O N S [Purificato et al., 2024, §2]
  • 30. NOV E L DE F I N I T I O N S User modeling (or user profiling) is the process of acquiring, extracting, and representing user features and personal characteristics to build accurate user models (or user profiles). It encompasses inferring personality traits and behaviors from user-generated data. This dynamic practice includes automatically converting user information into interpretable formats, capturing latent interests, and learning conceptual user representations. Essentially, user modeling constitutes the methodology for building and modifying user models, determining “what” to represent and “how” to effectively represent this information for adaptive and personalized systems. [Purificato et al., 2024] [Purificato et al., 2024, §2]
  • 32. EXPLICIT USER MODELING Also known as static or factual modeling, required direct input from the user, such as filling out a questionnaire or completing an online form. [Purificato et al., 2024, §3]
  • 33. IMPLICIT USER MODELING Initially used together with explicit methods, modern systems shifted to passive collection and analysis of dynamic user data, thus called behavioral and adaptive modeling. Static data still used by exploiting information previously shared (e.g., social network accounts) defining the novel pseudo-explicit user modeling. [Purificato et al., 2024, §3]
  • 34. DIRECT USER PREFERENCES Traditionally, user preferences and interests have been modelled using explicit and direct feedback. [Purificato et al., 2024, §3]
  • 35. INDIRECT USER PREFERENCES Everyday use of digital platforms and reluctance of users in providing direct feedback led to a growing emphasis on capturing an individual’s preferences and interests hidden in users’ historical behaviors. [Purificato et al., 2024, §3]
  • 36. USER BEHAVIORAL MODELING MULTI-BEHAVIOR MODELING Integrates various forms of user interactions with items, rather than relying on a single type of interaction. MICRO AND MACRO BEHAVIORAL MODELING Respectively, the immediate actions that a user takes reflecting short-term preferences, and large-scale actions that reflect a user’s long-term commitment. SEQUENTIAL BEHAVIOR MODELING Considers the order and timing of user actions as influential for modifying user interests. HIERARCHICAL USER PROFILING Models users’ real-time interests at various levels of granularity. MOBILE USER PROFILING Involves discerning users’ interests and behavioral patterns based on their activities on mobile devices. [Purificato et al., 2024, §3]
  • 37. SPECIFIC USER REPRESENTATION Scarcity of studies on generalized user model representation. Researchers tend to focus on specific aspects of user modeling rather than a holistic approach. [Purificato et al., 2024, §3]
  • 38. UNIVERSAL USER REPRESENTATION Create a unified and generalized profile of a user by encapsulating a broad spectrum of user behaviors and preferences without bias toward any specific task. [Purificato et al., 2024, §3]
  • 39. HOLISTIC USER MODELING Integrates diverse and heterogeneous personal data sources to construct a comprehensive user representation. [Purificato et al., 2024, §3]
  • 40. GRAPH DATA STRUCTURES KNOWLEDGE GRAPHS Specific type of graph structure that effectively represents complex information by accumulating and conveying knowledge of the real world, making them particularly useful in several contexts. GRAPH STRUCTURES In the context of user modeling, graph structures can be used to represent user behavior, preferences, and interactions by leveraging nodes and edges as, respectively, the users and the relationships among them. [Purificato et al., 2024, §3]
  • 41. DEEP LEARNING These models have significantly contributed to advancements in user modeling research field, enabling more accurate and comprehensive profiling and prediction of user behavior. Attention Mechanism Convolutional Neural Networks Autoencoders Graph Neural Networks Recurrent Neural Networks Transformers Long-Short Term Memory [Purificato et al., 2024, §3]
  • 42. STANDARD APPLICATIONS Versatile and valuable approaches in many fields, particularly where user-specific services are crucial, e.g.: o Recommender Systems; ; o E-commerce and Marketing; o User Interface Adaptation. . [Purificato et al., 2024, §3]
  • 43. CONTEMPORARY APPLICATIONS Innovative approaches applied in various important research fields to tackle state-of-the-art challenges, e.g.: o Fake news detections; o Social Network analysis; o Cybersecurity. [Purificato et al., 2024, §3]
  • 44. BEYOND- ACCURACY PERSPECTIVES Similar to the transformation observed in deep learning, the incorporation of advanced techniques extending beyond mere accuracy marks a significant global shift in various domains, including user modeling. These approaches prioritize fundamental values for humans, e.g., privacy, fairness, and transparency. [Purificato et al., 2024, §3]
  • 45. Special Issue “User Perspectives in Human-Centered Artificial Intelligence” International Journal of Human-Computer Studies (Q1) Call for papers Submission deadline: November 18, 2024
  • 46. CONTACTS Erasmo Purificato erasm o. p urificat o@ acm .org Ludovico Boratto ludo vico.b orat t o@ acm .o rg Ernesto W. De Luca ern est o. deluca@ ovgu.de Resources
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