8,069 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
A Survey on Conversational Search and Applications in Biomedicine
This paper aims to provide a radical rundown on Conversation Search
(ConvSearch), an approach to enhance the information retrieval method where
users engage in a dialogue for the information-seeking tasks. In this survey,
we predominantly focused on the human interactive characteristics of the
ConvSearch systems, highlighting the operations of the action modules, likely
the Retrieval system, Question-Answering, and Recommender system. We labeled
various ConvSearch research problems in knowledge bases, natural language
processing, and dialogue management systems along with the action modules. We
further categorized the framework to ConvSearch and the application is directed
toward biomedical and healthcare fields for the utilization of clinical social
technology. Finally, we conclude by talking through the challenges and issues
of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an
integrated and unified vision of the ConvSearch components from different
fields, which benefit the information-seeking process in healthcare systems
On intelligible multimodal visual analysis
Analyzing data becomes an important skill in a more and more digital world. Yet, many users are facing knowledge barriers preventing them to independently conduct their data analysis. To tear down some of these barriers, multimodal interaction for visual analysis has been proposed. Multimodal interaction through speech and touch enables not only experts, but also novice users to effortlessly interact with such kind of technology. However, current approaches do not take the user differences into account. In fact, whether visual analysis is intelligible ultimately depends on the user.
In order to close this research gap, this dissertation explores how multimodal visual analysis can be personalized. To do so, it takes a holistic view. First, an intelligible task space of visual analysis tasks is defined by considering personalization potentials. This task space provides an initial basis for understanding how effective personalization in visual analysis can be approached. Second, empirical analyses on speech commands in visual analysis as well as used visualizations from scientific publications further reveal patterns and structures. These behavior-indicated findings help to better understand expectations towards multimodal visual analysis. Third, a technical prototype is designed considering the previous findings. Enriching the visual analysis by a persistent dialogue and a transparency of the underlying computations, conducted user studies show not only advantages, but address the relevance of considering the user’s characteristics. Finally, both communications channels – visualizations and dialogue – are personalized. Leveraging linguistic theory and reinforcement learning, the results highlight a positive effect of adjusting to the user. Especially when the user’s knowledge is exceeded, personalizations helps to improve the user experience.
Overall, this dissertations confirms not only the importance of considering the user’s characteristics in multimodal visual analysis, but also provides insights on how an intelligible analysis can be achieved. By understanding the use of input modalities, a system can focus only on the user’s needs. By understanding preferences on the output modalities, the system can better adapt to the user. Combining both directions imporves user experience and contributes towards an intelligible multimodal visual analysis
Generating User-Centred Explanations via Illocutionary Question Answering: From Philosophy to Interfaces
We propose a new method for generating explanations with Artificial Intelligence (AI) and a tool to test its expressive power within a user interface. In order to bridge the gap between philosophy and human-computer interfaces, we show a new approach for the generation of interactive explanations based on a sophisticated pipeline of AI algorithms for structuring natural language documents into knowledge graphs, answering questions effectively and satisfactorily. With this work, we aim to prove that the philosophical theory of explanations presented by Achinstein can be actually adapted for being implemented into a concrete software application, as an interactive and illocutionary process of answering questions. Specifically, our contribution is an approach to frame illocution in a computer-friendly way, to achieve user-centrality with statistical question answering. Indeed, we frame the illocution of an explanatory process as that mechanism responsible for anticipating the needs of the explainee in the form of unposed, implicit, archetypal questions, hence improving the user-centrality of the underlying explanatory process. Therefore, we hypothesise that if an explanatory process is an illocutionary act of providing content-giving answers to questions, and illocution is as we defined it, the more explicit and implicit questions can be answered by an explanatory tool, the more usable (as per ISO 9241-210) its explanations. We tested our hypothesis with a user-study involving more than 60 participants, on two XAI-based systems, one for credit approval (finance) and one for heart disease prediction (healthcare). The results showed that increasing the illocutionary power of an explanatory tool can produce statistically significant improvements (hence with a P value lower than .05) on effectiveness. This, combined with a visible alignment between the increments in effectiveness and satisfaction, suggests that our understanding of illocution can be correct, giving evidence in favour of our theory
INVESTIGATING THE IMPACT OF ONLINE HUMAN COLLABORATION IN EXPLANATION OF AI SYSTEMS
An important subdomain in research on Human-Artificial Intelligence interaction is Explainable AI (XAI). XAI aims to improve human understanding and trust in machine intelligence and automation by providing users with visualizations and other information explaining the AI’s decisions, actions, or plans and thereby to establish justified trust and reliance. XAI systems have primarily used algorithmic approaches designed to generate explanations automatically that help understanding underlying information about decisions and establish justified trust and reliance, but an alternate that may augment these systems is to take advantage of the fact that user understanding of AI systems often develops through self-explanation (Mueller et al., 2021). Users attempt to piece together different sources of information and develop a clearer understanding, but these self-explanations are often lost if not shared with others. This thesis research demonstrated how this ‘Self-Explanation’ could be shared collaboratively via a system that is called collaborative XAI (CXAI). It is akin to a Social Q&A platform (Oh, 2018) such as StackExchange. A web-based system was built and evaluated formatively and via user studies. Formative evaluation will show how explanations in an XAI system, especially collaborative explanations, can be assessed based on ‘goodness criteria’ (Mueller et al., 2019). This thesis also investigated how the users performed with the explanations from this type of XAI system. Lastly, the research investigated whether the users of CXAI system are satisfied with the human-generated explanations generated in the system and check if the users can trust this type of explanation
Datasets for Large Language Models: A Comprehensive Survey
This paper embarks on an exploration into the Large Language Model (LLM)
datasets, which play a crucial role in the remarkable advancements of LLMs. The
datasets serve as the foundational infrastructure analogous to a root system
that sustains and nurtures the development of LLMs. Consequently, examination
of these datasets emerges as a critical topic in research. In order to address
the current lack of a comprehensive overview and thorough analysis of LLM
datasets, and to gain insights into their current status and future trends,
this survey consolidates and categorizes the fundamental aspects of LLM
datasets from five perspectives: (1) Pre-training Corpora; (2) Instruction
Fine-tuning Datasets; (3) Preference Datasets; (4) Evaluation Datasets; (5)
Traditional Natural Language Processing (NLP) Datasets. The survey sheds light
on the prevailing challenges and points out potential avenues for future
investigation. Additionally, a comprehensive review of the existing available
dataset resources is also provided, including statistics from 444 datasets,
covering 8 language categories and spanning 32 domains. Information from 20
dimensions is incorporated into the dataset statistics. The total data size
surveyed surpasses 774.5 TB for pre-training corpora and 700M instances for
other datasets. We aim to present the entire landscape of LLM text datasets,
serving as a comprehensive reference for researchers in this field and
contributing to future studies. Related resources are available at:
https://github.com/lmmlzn/Awesome-LLMs-Datasets.Comment: 181 pages, 21 figure
Recommended from our members
Proceedings of QG2010: The Third Workshop on Question Generation
These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge".
QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)
- …