7,537 research outputs found
You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems
Visual query systems (VQSs) empower users to interactively search for line
charts with desired visual patterns, typically specified using intuitive
sketch-based interfaces. Despite decades of past work on VQSs, these efforts
have not translated to adoption in practice, possibly because VQSs are largely
evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we
collaborated with experts from three diverse domains---astronomy, genetics, and
material science---via a year-long user-centered design process to develop a
VQS that supports their workflow and analytical needs, and evaluate how VQSs
can be used in practice. Our study results reveal that ad-hoc sketch-only
querying is not as commonly used as prior work suggests, since analysts are
often unable to precisely express their patterns of interest. In addition, we
characterize three essential sensemaking processes supported by our enhanced
VQS. We discover that participants employ all three processes, but in different
proportions, depending on the analytical needs in each domain. Our findings
suggest that all three sensemaking processes must be integrated in order to
make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25
in Vancouver, Canada. Paper will also be published in a special issue of IEEE
Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS
(InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing,
Visualization, Visualization design and evaluation method
Leveraging Large Language Models for Sequential Recommendation
Sequential recommendation problems have received increasing attention in
research during the past few years, leading to the inception of a large variety
of algorithmic approaches. In this work, we explore how large language models
(LLMs), which are nowadays introducing disruptive effects in many AI-based
applications, can be used to build or improve sequential recommendation
approaches. Specifically, we devise and evaluate three approaches to leverage
the power of LLMs in different ways. Our results from experiments on two
datasets show that initializing the state-of-the-art sequential recommendation
model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20%
compared to the vanilla BERT4Rec model. Furthermore, we find that a simple
approach that leverages LLM embeddings for producing recommendations, can
provide competitive performance by highlighting semantically related items. We
publicly share the code and data of our experiments to ensure reproducibility.Comment: 9 page
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