292,410 research outputs found
AI-Copilot for Business Optimisation: A Framework and A Case Study in Production Scheduling
Business optimisation refers to the process of finding and implementing
efficient and cost-effective means of operation to bring a competitive
advantage for businesses. Synthesizing problem formulations is an integral part
of business optimisation, which relies on human expertise to construct problem
formulations using optimisation languages. Interestingly, with advancements in
Large Language Models (LLMs), the human expertise needed in problem formulation
can be minimized. However, developing an LLM for problem formulation is
challenging, due to training data, token limitations, and lack of appropriate
performance metrics. For the requirement of training data, recent attention has
been directed towards fine-tuning pre-trained LLMs for downstream tasks rather
than training an LLM from scratch for a specific task. In this paper, we adopt
an LLM fine-tuning approach and propose an AI-Copilot for business optimisation
problem formulation. For token limitations, we introduce modularization and
prompt engineering techniques to synthesize complex problem formulations as
modules that fit into the token limits of LLMs. Additionally, we design
performance evaluation metrics that are better suited for assessing the
accuracy and quality of problem formulations. The experiment results
demonstrate that with this approach we can synthesize complex and large problem
formulations for a typical business optimisation problem in production
scheduling
Inferring semantic relations by user feedback
In the last ten years, ontology-based recommender systems have been shown to be effective tools for predicting user preferences and suggesting items. There are however some issues associated with the ontologies adopted by these approaches, such as: 1) their crafting is not a cheap process, being time consuming and calling for specialist expertise; 2) they may not represent accurately the viewpoint of the targeted user community; 3) they tend to provide rather static models, which fail to keep track of evolving user perspectives. To address these issues, we propose Klink UM, an approach for extracting emergent semantics from user feedbacks, with the aim of tailoring the ontology to the users and improving the recommendations accuracy. Klink UM uses statistical and machine learning techniques for finding hierarchical and similarity relationships between keywords associated with rated items and can be used for: 1) building a conceptual taxonomy from scratch, 2) enriching and correcting an existing ontology, 3) providing a numerical estimate of the intensity of semantic relationships according to the users. The evaluation shows that Klink UM performs well with respect to handcrafted ontologies and can significantly increase the accuracy of suggestions in content-based recommender systems
Broad expertise retrieval in sparse data environments
Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings
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