101,969 research outputs found
Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization
In this paper we study the personalized text search problem. The keyword
based search method in conventional algorithms has a low efficiency in
understanding users' intention since the semantic meaning, user profile, user
interests are not always considered. Firstly, we propose a novel text search
algorithm using a inverse filtering mechanism that is very efficient for label
based item search. Secondly, we adopt the Bayesian network to implement the
user interest prediction for an improved personalized search. According to user
input, it searches the related items using keyword information, predicted user
interest. Thirdly, the word vectorization is used to discover potential targets
according to the semantic meaning. Experimental results show that the proposed
search engine has an improved efficiency and accuracy and it can operate on
embedded devices with very limited computational resources
Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education
This paper presents a novel framework, Artificial Intelligence-Enabled
Intelligent Assistant (AIIA), for personalized and adaptive learning in higher
education. The AIIA system leverages advanced AI and Natural Language
Processing (NLP) techniques to create an interactive and engaging learning
platform. This platform is engineered to reduce cognitive load on learners by
providing easy access to information, facilitating knowledge assessment, and
delivering personalized learning support tailored to individual needs and
learning styles. The AIIA's capabilities include understanding and responding
to student inquiries, generating quizzes and flashcards, and offering
personalized learning pathways. The research findings have the potential to
significantly impact the design, implementation, and evaluation of AI-enabled
Virtual Teaching Assistants (VTAs) in higher education, informing the
development of innovative educational tools that can enhance student learning
outcomes, engagement, and satisfaction. The paper presents the methodology,
system architecture, intelligent services, and integration with Learning
Management Systems (LMSs) while discussing the challenges, limitations, and
future directions for the development of AI-enabled intelligent assistants in
education.Comment: 29 pages, 10 figures, 9659 word
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Q&A Platforms Evaluated Using Butler University Q&A Intelligence Index
A new study using the Butler University Q&A Intelligence Index measures how various mobile Q&A platforms deliver quality, accurate answers in a timely manner to a broad variety of questions. Based on the results of our analysis, ChaCha led all Q&A platforms on mobile devices.
Results of the study are based upon review of a large set of responses from each of the major Q&A platforms, coupled with a comparison of disparate Q&A platforms that serve answers in different ways. Our methodology included the creation of a new metric, termed the Butler University Q&A Intelligence Index, which measures the likelihood that a user can expect to receive a correct answer in a timely manner to any random question asked using natural language. We asked questions via mobile services and randomized the questions to cover both popular and long-tail knowledge requests
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