478 research outputs found
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Representation learning on networks aims to derive a meaningful vector
representation for each node, thereby facilitating downstream tasks such as
link prediction, node classification, and node clustering. In heterogeneous
text-rich networks, this task is more challenging due to (1) presence or
absence of text: Some nodes are associated with rich textual information, while
others are not; (2) diversity of types: Nodes and edges of multiple types form
a heterogeneous network structure. As pretrained language models (PLMs) have
demonstrated their effectiveness in obtaining widely generalizable text
representations, a substantial amount of effort has been made to incorporate
PLMs into representation learning on text-rich networks. However, few of them
can jointly consider heterogeneous structure (network) information as well as
rich textual semantic information of each node effectively. In this paper, we
propose Heterformer, a Heterogeneous Network-Empowered Transformer that
performs contextualized text encoding and heterogeneous structure encoding in a
unified model. Specifically, we inject heterogeneous structure information into
each Transformer layer when encoding node texts. Meanwhile, Heterformer is
capable of characterizing node/edge type heterogeneity and encoding nodes with
or without texts. We conduct comprehensive experiments on three tasks (i.e.,
link prediction, node classification, and node clustering) on three large-scale
datasets from different domains, where Heterformer outperforms competitive
baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer
Congenial Web Search : A Conceptual Framework for Personalized, Collaborative, and Social Peer-to-Peer Retrieval
Traditional information retrieval methods fail to address the fact that information consumption and production are social activities. Most Web search engines do not consider the social-cultural environment of users' information needs and the collaboration between users. This dissertation addresses a new search paradigm for Web information retrieval denoted as Congenial Web Search. It emphasizes personalization, collaboration, and socialization methods in order to improve effectiveness. The client-server architecture of Web search engines only allows the consumption of information. A peer-to-peer system architecture has been developed in this research to improve information seeking. Each user is involved in an interactive process to produce meta-information. Based on a personalization strategy on each peer, the user is supported to give explicit feedback for relevant documents. His information need is expressed by a query that is stored in a Peer Search Memory. On one hand, query-document associations are incorporated in a personalized ranking method for repeated information needs. The performance is shown in a known-item retrieval setting. On the other hand, explicit feedback of each user is useful to discover collaborative information needs. A new method for a controlled grouping of query terms, links, and users was developed to maintain Virtual Knowledge Communities. The quality of this grouping represents the effectiveness of grouped terms and links. Both strategies, personalization and collaboration, tackle the problem of a missing socialization among searchers. Finally, a concept for integrated information seeking was developed. This incorporates an integrated representation to improve effectiveness of information retrieval and information filtering. An integrated information retrieval process explores a virtual search network of Peer Search Memories in order to accomplish a reputation-based ranking. In addition, the community structure is considered by an integrated information filtering process. Both concepts have been evaluated and shown to have a better performance than traditional techniques. The methods presented in this dissertation offer the potential towards more transparency, and control of Web search
Inferring user interests in microblogging social networks: a survey
With the growing popularity of microblogging services such as Twitter in recent years,
an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications
and areas. Inferring user interests plays a significant role in providing personalized
recommendations on microblogging services, and also on third-party applications
providing social logins via these services, especially in cold-start situations. In this
survey, we review user modeling strategies with respect to inferring user interests
from previous studies. To this end, we focus on four dimensions of inferring user
interest profiles: (1) data collection, (2) representation of user interest profiles, (3)
construction and enhancement of user interest profiles, and (4) the evaluation of the
constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging
social networks with respect to the four dimensions. For each dimension, we review
and summarize previous studies based on specified criteria. Finally, we discuss some
challenges and opportunities for future work in this research domain
Modelado de perfiles de usuario para la recomendación de contenido en Twitter
En este trabajo se investigan diferentes mecanismos para deducir la semántica de los mensajes de Twitter con el fin de modelar perfiles de usuario. Se introducen y analizan métodos de procesamiento de lenguaje natural para plantear diferentes formas de inferir los intereses de los usuarios a partir de sus tweets. Luego, esas estrategias son comparadas para analizar el comportamiento al recomendar mensajes de otros usuarios.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Modelado de perfiles de usuario para la recomendación de contenido en Twitter
En este trabajo se investigan diferentes mecanismos para deducir la semántica de los mensajes de Twitter con el fin de modelar perfiles de usuario. Se introducen y analizan métodos de procesamiento de lenguaje natural para plantear diferentes formas de inferir los intereses de los usuarios a partir de sus tweets. Luego, esas estrategias son comparadas para analizar el comportamiento al recomendar mensajes de otros usuarios.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Modelado de perfiles de usuario para la recomendación de contenido en Twitter
En este trabajo se investigan diferentes mecanismos para deducir la semántica de los mensajes de Twitter con el fin de modelar perfiles de usuario. Se introducen y analizan métodos de procesamiento de lenguaje natural para plantear diferentes formas de inferir los intereses de los usuarios a partir de sus tweets. Luego, esas estrategias son comparadas para analizar el comportamiento al recomendar mensajes de otros usuarios.Sociedad Argentina de Informática e Investigación Operativa (SADIO
Applicability of artificial intelligence in e-commerce fashion platforms
A inovação tecnológica e a democratização da inteligência
artificial (IA) têm vindo a alavancar o potencial de sucesso em
todas as áreas que conhecemos hoje, com expectativas do que
ainda está para vir. A presente dissertação propõe uma análise
das aplicações da IA na indústria da moda, particularmente nas
plataformas de marcas de moda do comércio eletrónico, e de
que forma está a ter impacto na esfera pessoal do consumidor,
particularmente no processo de tomada de decisão dos
consumidores da Geração Z. O âmbito da IA tem vindo a evoluir
de tal forma que permitiu às empresas não só melhorar a sua
oferta e a procura dos clientes, como também proporcionar uma
experiência de compra que vai para além da “seleção e compra”
mecânica: os pontos de contacto impulsionados pela IA
influenciam e enriquecem cada fase do processo de tomada de
decisão, seja de forma mais positiva ou negativa. Em última
análise, esta dissertação pretende proporcionar ao leitor um
melhor conhecimento sobre a IA e o comércio eletrónico de
moda, bem como delinear o seu impacto no comportamento
online do consumidor.Technological innovation and democratization of artificial
intelligence (AI) have been leveraging the potential success in
every field we know today, while more is yet to come. The
following dissertation proposes an analysis of AI achievements
within the fashion industry, particularly in e-commerce fashion
brand platforms, and how it is impacting the consumer personal
sphere, particularly the decision-making process of Gen-Z
consumers. The field of AI has been evolving in such a way that
allows companies to not only improve their supply and customer
demand, but also provide a shopping experience that goes
beyond the mechanical “select and buy“: AI-driven touchpoints
influence and enrich each stage of the decision-making process,
whether more positively or negatively. Ultimately, this dissertation
intends to provide the reader a better knowledge of AI and
fashion e-commerce joining applications, and to delineate its
impact on the online customer journey
Health State Estimation
Life's most valuable asset is health. Continuously understanding the state of
our health and modeling how it evolves is essential if we wish to improve it.
Given the opportunity that people live with more data about their life today
than any other time in history, the challenge rests in interweaving this data
with the growing body of knowledge to compute and model the health state of an
individual continually. This dissertation presents an approach to build a
personal model and dynamically estimate the health state of an individual by
fusing multi-modal data and domain knowledge. The system is stitched together
from four essential abstraction elements: 1. the events in our life, 2. the
layers of our biological systems (from molecular to an organism), 3. the
functional utilities that arise from biological underpinnings, and 4. how we
interact with these utilities in the reality of daily life. Connecting these
four elements via graph network blocks forms the backbone by which we
instantiate a digital twin of an individual. Edges and nodes in this graph
structure are then regularly updated with learning techniques as data is
continuously digested. Experiments demonstrate the use of dense and
heterogeneous real-world data from a variety of personal and environmental
sensors to monitor individual cardiovascular health state. State estimation and
individual modeling is the fundamental basis to depart from disease-oriented
approaches to a total health continuum paradigm. Precision in predicting health
requires understanding state trajectory. By encasing this estimation within a
navigational approach, a systematic guidance framework can plan actions to
transition a current state towards a desired one. This work concludes by
presenting this framework of combining the health state and personal graph
model to perpetually plan and assist us in living life towards our goals.Comment: Ph.D. Dissertation @ University of California, Irvin
A comparative study of recruitment and enrollment practices in colleges and universities using integrated marketing communications
A study was conducted to determine if using an integrated marketing communications (IMC) approach to college and university recruitment and enrollment helps institutions better achieve their enrollment goals.
This study involved a review of related research, in-depth interviews with education-specific marketing consulting firms, and admissions and marketing professionals at various institutions of higher learning. Primary research involved a 14-item self-administered questionnaire mailed to selected four-year colleges and universities across the country. The questionnaire asked institution executives familiarity and general institutional marketing questions, marketing activity questions, use of IMC, opinion inquiries and demographics.
Mean scores through coded responses, frequencies and percentages assessed resulting data.
Findings revealed a great deal of attention is devoted to IMC by many institutions. Colleges and universities using an integrated marketing approach to recruitment and enrollment believe their student yield has improved significantly since fully incorporating IMC strategies. Institutions not using an integrated approach to marketing communications feel they are not as successful at enrolling either the number nor the type of students desired for their school
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